pacman::p_load(sf, tidyverse, questionr, janitor, psych, ggplot2, gcookbook, tmap, ggpubr, corrplot, gtsummary, regclass, caret, heatmaply, ggdendro, cluster, factoextra, spdep, ClustGeo, GGally)Regionalisation with Spatially Constrained Cluster Analysis
case study : Regionalisation by Multivariate Water Point Attributes with Non-spatially Constrained and Spatially Constrained Clustering Methods.
1. OVERVIEW
Regionalisation with Spatially Constrained clustering analysis requires similar observations to be grouped according to their statistical attributes and spatial location.
This study focuses on regionalising analysis based on Nigeria’s water points attributes.
1.1 Objectives
Regionalise Nigeria by using the following measures :
Total number of water points by status, i.e. functional, non-functional, and unknown;
Percentage of water points by :
status (functional, non-functional, and unknown);
deployed water technology (hand pump, mechanical pump, stand tap, etc.) ;
usage capacity (1000, 300, 250, 50);
rural or urban.
1.2 Scope of Works
Some of the specific tasks for this study are :
import the shapefile into R with the appropriate sf method, and save it in a simple feature data frame format;
- note : three (3) Projected Coordinate Systems of Nigeria are : EPSG: 26391, 26392, and 26303.
derive the proportion of functional and non-functional water points at LGA level (i.e. ADM2) by appropriate tidyr and dplyr methods;
combine geospatial and aspatial data frames into a simple feature data frame.
delineate water points measures functional regions by using :
conventional hierarchical clustering.
spatially constrained clustering algorithms.
plot two (2) main types of maps below :
Thematic Mapping
Show the derived water-point measures by appropriate statistical graphics and choropleth mapping technique.
Analytical Mapping
Plot delineated functional regions using non-spatially constrained and spatially constrained clustering algorithms.
2. R PACKAGE REQUIRED
Following are the packages require for this exercise :
importing and Processing Geospatial Data
- sf
st_as_sfc( ) - 3.3.1
st_sf( ) - 3.3.2
st_read( ) - 3.4
st_join( ) - 3.5
- sf
importing and processing non-spatial data
tidyverse
readr
-- read_csv( ) - 3.2.1, 3.2.2.1
-- problems( ) - 3.2.1
-- write_rds( ) - 3.2.1.1, 3.2.2.3
-- read_rds( )- 3.2.1.1
dplyr
-- filter( ) - 3.2.2.1
-- left_join( ) -
-- add_count( ) - 3.3.1.3
-- select( ) - 3.4
-- count( ) -
tidyr
-- replace_na( ) - 3.
plot map for visualisation
tmap
- tmap_mode( ) -
- tm_shape( ) -
- tm_polygons( ) -
- tm_view( ) -
- tm_fill( ) -
- tm_borders( ) -
- tm_style( ) -
- tm_layout( ) -
- qtm( ) -
questionr
- freq( ) - 3.6.3
factoextra
stats
dist( ) - 5.1.2
base
- summary( ) - 3.2.1.2
- duplicated( ) - 3.4.3.3
2.1 Load R Packages into R Environment
Use the code chunk below.
3. GEOSPATIAL DATA
3.1 Acquire Data Source
Aspatial Data
- Download the Nigeria data set in shapefile format via Access WPdx+ Global Data Repository from WPdx Global Data Repositories.
- Rename the title of the data set to “geo_export”.
Geospatial Data
- Download the Nigeria geoBoundaries data set at ADM2 level from geoBoundaries.org or the Humanitarian Data Exchange portal.
- Rename the title of the data set to “nga_admbnda_adm2_osgof_20190417”
3.2 Import Aspatial Data
3.2.4 Create Master File
Usage of the code chunk below :
left_join( ) - dplyr - to combine wp_coord, wp_cond and wp_adm.
wp <- left_join((
left_join(
wp_coord,
wp_cond,
by = c("row_id")
)),
wp_adm, by = c("row_id"))3.2.5 Convert Well Known Text (WKT) Data to SF Data Frame
The “New Georeferenced Column” in wp_rds contains spatial data in a WKT format.
Two (2) steps to convert the WKT data format into an sf data frame.
3.2.5.1 derive new field :: “geometry”
Usage of the code chunk below :
st_as_sfc( ) - sf - to derive a new field “geometry”.
wp$geometry = st_as_sfc(wp$`New Georeferenced Column`)3.2.5.2 convert to SF Data Frame
Usage of the code chunk below :
st_sf( ) - sf - to convert the tibble data frame into sf data frame with crs first set to WGS 84 (EPSG : 4326).
st_crs( ) - sf - to retrieve coordinate reference system from the object.
wp_sf<- st_sf(wp, crs = 4326)
st_crs(wp_sf)Coordinate Reference System:
User input: EPSG:4326
wkt:
GEOGCRS["WGS 84",
ENSEMBLE["World Geodetic System 1984 ensemble",
MEMBER["World Geodetic System 1984 (Transit)"],
MEMBER["World Geodetic System 1984 (G730)"],
MEMBER["World Geodetic System 1984 (G873)"],
MEMBER["World Geodetic System 1984 (G1150)"],
MEMBER["World Geodetic System 1984 (G1674)"],
MEMBER["World Geodetic System 1984 (G1762)"],
MEMBER["World Geodetic System 1984 (G2139)"],
ELLIPSOID["WGS 84",6378137,298.257223563,
LENGTHUNIT["metre",1]],
ENSEMBLEACCURACY[2.0]],
PRIMEM["Greenwich",0,
ANGLEUNIT["degree",0.0174532925199433]],
CS[ellipsoidal,2],
AXIS["geodetic latitude (Lat)",north,
ORDER[1],
ANGLEUNIT["degree",0.0174532925199433]],
AXIS["geodetic longitude (Lon)",east,
ORDER[2],
ANGLEUNIT["degree",0.0174532925199433]],
USAGE[
SCOPE["Horizontal component of 3D system."],
AREA["World."],
BBOX[-90,-180,90,180]],
ID["EPSG",4326]]
3.2.5.3 retrieve geometry summary :: wp_sf
Usage of the code chunk below :
st_geometry( ) - sf - to get the geometry summary.
st_geometry(wp_sf)Geometry set for 95008 features
Geometry type: POINT
Dimension: XY
Bounding box: xmin: 2.707441 ymin: 4.301812 xmax: 14.21828 ymax: 13.86568
Geodetic CRS: WGS 84
First 5 geometries:
POINT (6.95009 6.78599)
POINT (7.604793 6.78321)
POINT (7.60024 6.759284)
POINT (7.615451 6.799595)
POINT (7.65991 6.762375)
3.3 Import Boundary Data of Nigeria LGA
Usage of the code chunk below :
st_read( ) - sf - to read simple features.
select( ) - dplyr - to select “shapeName” variable.
bdy_nga <- st_read(dsn = "/jephOstan/ISSS624/class_project/project_2/data/geospatial",
layer = "geoBoundaries-NGA-ADM2",
crs = 4326) %>%
select(shapeName)Reading layer `geoBoundaries-NGA-ADM2' from data source
`D:\jephOstan\ISSS624\class_project\project_2\data\geospatial'
using driver `ESRI Shapefile'
Simple feature collection with 774 features and 5 fields
Geometry type: MULTIPOLYGON
Dimension: XY
Bounding box: xmin: 2.668534 ymin: 4.273007 xmax: 14.67882 ymax: 13.89442
Geodetic CRS: WGS 84
problems(bdy_nga)3.3.1 Review Imported File
3.3.1.1 check for missing data
freq.na(bdy_nga$shapeName)missing %
0 0
3.3.1.2 check for duplication :: “shapeName”
Usage of the code chunk below :
duplicated( ) - base - to determine duplicate elements.
freq(duplicated(bdy_nga$shapeName)) n % val%
FALSE 768 99.2 99.2
TRUE 6 0.8 0.8
3.3.1.3 list the duplicated value :: “shapeName”
Usage of the code chunk below :
add_count( ) - dplyr - to count observations by group
wp_duplShapeName <- bdy_nga %>%
add_count(bdy_nga$shapeName) %>%
filter(n!=1) %>%
select(-n)
wp_duplShapeNameSimple feature collection with 12 features and 2 fields
Geometry type: MULTIPOLYGON
Dimension: XY
Bounding box: xmin: 3.316459 ymin: 6.459038 xmax: 9.020704 ymax: 12.05035
Geodetic CRS: WGS 84
First 10 features:
shapeName bdy_nga$shapeName geometry
1 Bassa Bassa MULTIPOLYGON (((6.708541 7....
2 Bassa Bassa MULTIPOLYGON (((8.823522 10...
3 Ifelodun Ifelodun MULTIPOLYGON (((4.664107 8....
4 Ifelodun Ifelodun MULTIPOLYGON (((4.721977 7....
5 Irepodun Irepodun MULTIPOLYGON (((5.05493 8.0...
6 Irepodun Irepodun MULTIPOLYGON (((4.543349 7....
7 Nasarawa Nasarawa MULTIPOLYGON (((8.554589 11...
8 Nasarawa Nasarawa MULTIPOLYGON (((7.493228 8....
9 Obi Obi MULTIPOLYGON (((8.191919 6....
10 Obi Obi MULTIPOLYGON (((9.008576 8....
3.3.1.4 verify findings in section 3.3.1.3
Usage of the code chunk below :
tmap_mode( ) - tmap - to set tmap mode to static plotting or interactive.
tm_shape( ) - tmap - to specify the shape object.
tm_polygons( ) - tmap - to fill the polygons and draw the polygon borders.
tm_view( ) - tmap - to set the options for the interactive tmap viewer.
tm_fill( ) - tmap - to specify either which colour to be used or which data variable mapped to the colour palette.
tm_borders( ) - tmap - to draw the polygon borders.
tmap_style( ) - tmap - to set the tmap style.
tm_layout( ) - tmap - to set the layout of cartographic map.
tmap_mode("view")tmap mode set to interactive viewing
tm_shape(bdy_nga)+
tm_polygons()+
tm_view(set.zoom.limits = c(6,8))+
tm_shape(wp_duplShapeName)+
tm_fill("shapeName",
n = 6,
style = "jenks")+
tm_borders(alpha = 0.5)+
tmap_style("albatross")+
tm_layout(main.title = "Distribution of Duplicated ShapeName",
main.title.size = 1.3,
main.title.position = "center")tmap style set to "albatross"
other available styles are: "white", "gray", "natural", "cobalt", "col_blind", "beaver", "bw", "classic", "watercolor"
Remarks :
The plot above indicates those duplicated water points are from different Nigeria states.
tmap_mode("plot")tmap mode set to plotting
3.3.1.5 acquire State info for duplicated value
The State info to be combined with the duplicated “shapeName”. This will make all the shapeName unique.
| lga | row_id | headquarter | state | iso3166code | state_dd_coordinates |
|---|---|---|---|---|---|
| Bassa | 94 | Oguma | Kogi | NG.KO.BA | 7.75 6.75 |
| Bassa | 95 | Bassa | Plateau | NG.PL.BA | 9.16667 9.75 |
| Ifelodun | 304 | Share | Kwara | NG.KW.IF | 8.5 5.0 |
| Ifelodun | 305 | Ikirun | Osun | NG.OS.ID | 7.5 4.5 |
| Irepodun | 355 | Omu Aran | Kwara | NG.KW.IR | 8.5 5.0 |
| Irepodun | 356 | Ilobu | Osun | NG.OS.IP | 7.5 4.5 |
| Nasarawa | 519 | Bompai | Kano | NG.KN.NA | 11.5 8.5 |
| Nasarawa | 520 | Nasarawa | Nasarawa | NG.NA.NA | 8.53 7.7 |
| Obi | 546 | Obi | Nasarawa | NG.NA.OB | 8.53 7.7 |
| Obi | 547 | Obarike-Ito | Benue | NG.BE.OB | 7.33333 8.75 |
| Surelere | 693 | Surelere | Lagos | NG.LA.SU | 6.5 3.35 |
| Surelere | 694 | Iresa-Adu | Oyo | NG.OY.SU | 8.07 4.41 |
3.4 Data Wrangling
3.4.1 Edit Duplicated Value :: “shapeName”
bdy_nga$shapeName[c(94,95,304,305,355,356,519,546,547,693,694)] <-
c("Bassa Kogi",
"Bassa Plateau",
"Ifelodun Kwara",
"Ifelodun Osun",
"Irepodun Kwara",
"Irepodun Osun",
"Nasarawa Kano",
"Nasarawa Nasarawa",
"Obi Nasarawa",
"Obi Benue",
"Surulere Lagos",
"Surulere Oyo")Warning in bdy_nga$shapeName[c(94, 95, 304, 305, 355, 356, 519, 546, 547, :
number of items to replace is not a multiple of replacement length
3.4.1.1 validate edited value :: “shapeName”
wp_duplShapeName1 <- bdy_nga %>%
add_count(bdy_nga$shapeName) %>%
filter(n!=1) %>%
select(-n)
wp_duplShapeName1Simple feature collection with 0 features and 2 fields
Bounding box: xmin: NA ymin: NA xmax: NA ymax: NA
Geodetic CRS: WGS 84
[1] shapeName bdy_nga$shapeName geometry
<0 rows> (or 0-length row.names)
3.4.2 Perform Point-in-Polygon Overlay
This step combine both attribute and boundary of the water points into a simple feature object.
3.4.2.1 join Objects :: wp_sf and bdy_nga
Usage of the code chunk below :
st_join( ) - sf - to join sf-class objects based on geometry, namely, wp_sf and bdy_nga.
wp_joined <- st_join(wp_sf, bdy_nga)3.4.2.2 save and read RDS File :: wp_joined
write_rds(wp_joined,"/jephOstan/ISSS624/class_project/project_2/data/geodata/wp_joined.rds",compress = "xz")
wp_joined <- read_rds("/jephOstan/ISSS624/class_project/project_2/data/geodata/wp_joined.rds")3.4.2.3 inspect joined file :: wp_joined
st_geometry(wp_joined)Geometry set for 95008 features
Geometry type: POINT
Dimension: XY
Bounding box: xmin: 2.707441 ymin: 4.301812 xmax: 14.21828 ymax: 13.86568
Geodetic CRS: WGS 84
First 5 geometries:
POINT (6.95009 6.78599)
POINT (7.604793 6.78321)
POINT (7.60024 6.759284)
POINT (7.615451 6.799595)
POINT (7.65991 6.762375)
-- determine reference point :: “shapeName” or “clean_adm2”
wp_refLga <- (wp_joined$shapeName == wp_joined$clean_adm2)
freq(wp_refLga) n % val%
FALSE 29713 31.3 31.3
TRUE 65266 68.7 68.7
NA 29 0.0 NA
Remarks :
There are 29,713 of “FALSE”, which is more than 30% of local government areas’ name mismatched between “shapeName” and “clean_adm2”.
Unlike the Water Point Data Exchange data that involved multitple parties, the geoBoundaries data is sourced from “geoBoundaries: A global database of political administrative boundaries.” Plos one 15, no. 4 (2020): e0231866,
- Hence, the “shapeName” to be used throughout this study.
-- assess uniqueness of each Water Point
wp_joined %>% janitor::get_dupes(shapeName,lat_lon_deg)No duplicate combinations found of: shapeName, lat_lon_deg
Simple feature collection with 0 features and 24 fields
Bounding box: xmin: NA ymin: NA xmax: NA ymax: NA
Geodetic CRS: WGS 84
# A tibble: 0 × 25
# … with 25 variables: shapeName <chr>, lat_lon_deg <chr>, dupe_count <int>,
# row_id <dbl>, lat_deg <dbl>, lon_deg <dbl>, New Georeferenced Column <chr>,
# water_source <chr>, water_source_clean <chr>, water_source_category <chr>,
# water_tech_clean <chr>, water_tech_category <chr>, status_clean <chr>,
# status <chr>, clean_adm1 <chr>, clean_adm2 <chr>,
# water_point_population <dbl>, local_population_1km <dbl>,
# crucialness_score <dbl>, pressure_score <dbl>, usage_capacity <dbl>, …
Remarks :
Each water point observation is unique as there are no duplicate combination of “shapeName” together with “lat_lon_deg”.
-- reveal value :: “status_clean”
freq(wp_joined$status_clean) n % val%
Abandoned 175 0.2 0.2
Abandoned/Decommissioned 234 0.2 0.3
Functional 45883 48.3 54.4
Functional but needs repair 4579 4.8 5.4
Functional but not in use 1686 1.8 2.0
Non-Functional 29385 30.9 34.8
Non-Functional due to dry season 2403 2.5 2.8
Non functional due to dry season 7 0.0 0.0
NA 10656 11.2 NA
-- reveal value :: “crucialness_score”
summary(wp_joined$crucialness_score) Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
0.000 0.130 0.304 0.414 0.628 1.000 6879
-- reveal value :: “is_urban”
freq(wp_joined$is_urban) n % val%
FALSE 75444 79.4 79.4
TRUE 19564 20.6 20.6
-- reveal value :: “water_tech_category”
freq(wp_joined$water_tech_category) n % val%
Hand Pump 58755 61.8 69.2
Mechanized Pump 25644 27.0 30.2
Rope and Bucket 1 0.0 0.0
Tapstand 553 0.6 0.7
NA 10055 10.6 NA
-- reveal value :: “usage_capacity”
freq(wp_joined$usage_capacity) n % val%
50 2 0.0 0.0
250 573 0.6 0.6
300 68789 72.4 72.4
1000 25644 27.0 27.0
3.4.3 Replace “NA” with “Unknown”
mutate( ) - dplyr - to run replace_na( ) function.
- replace_na( ) - tidyr - to replace NAs with “unknown”.
wp_joined1 <- wp_joined %>%
mutate(status_clean = replace_na(status_clean, "Unknown")) %>%
mutate(water_tech_category = replace_na(water_tech_category, "Unknown")) %>%
mutate(status = replace_na(status, "Unknown")) %>%
mutate(water_point_population = replace_na(water_point_population, 0)) %>%
mutate(local_population_1km = replace_na(local_population_1km, 0)) %>%
mutate(crucialness_score = replace_na(crucialness_score, 0)) %>%
mutate(pressure_score = replace_na(pressure_score, 0))3.4.4 Standardise Value
3.4.4.1 combine value :: “status_clean”
wp_joined1 <- wp_joined1 %>%
mutate(status_clean = str_replace(status_clean,"Non functional due to dry season" ,"Non-Functional due to dry season")) %>%
mutate(status_clean = str_replace(status_clean,"Abandoned/Decommissioned/Decommissioned","Abandoned/Decommissioned"))3.4.4.2 review “status_clean”
freq(wp_joined1$status_clean)3.4.4.3 read RDS file :: wp_joined1
wp_joined1 <- read_rds("/jephOstan/ISSS624/class_project/project_2/data/geodata/wp_joined1.rds")3.4.5 Extract Water Point for New Table :: wp_nga
3.4.5.1 extract functional water point
wpt_functional <- wp_joined1 %>%
filter(status_clean %in%
c("Functional",
"Functional but not in use",
"Functional but needs repair"))-- save and read RDS file :: wpt_functional
write_rds(wpt_functional,"/jephOstan/ISSS624/class_project/project_2/data/geodata/wpt_functional.rds",compress = "xz")
wpt_functional <- read_rds("/jephOstan/ISSS624/class_project/project_2/data/geodata/wpt_functional.rds")3.4.5.2 inspect variable and value
-- reveal value :: “status_clean”
freq(wpt_functional$status_clean) n % val%
Functional 45883 88.0 88.0
Functional but needs repair 4579 8.8 8.8
Functional but not in use 1686 3.2 3.2
length(wpt_functional$row_id)[1] 52148
length(wpt_functional$row_id)/length(wp_joined1$row_id)*100[1] 54.88801
Remarks :
The total functional water points is 52,148 which is about 54.89% of total water points.
-- reveal value :: “usage_capacity”
freq(wpt_functional$usage_capacity) n % val%
50 2 0.0 0.0
250 75 0.1 0.1
300 38064 73.0 73.0
1000 14007 26.9 26.9
-- reveal value “usage_capacity” by “status_clean”
wpt_functional %>% count(status_clean, usage_capacity, sort = TRUE)Simple feature collection with 10 features and 3 fields
Geometry type: MULTIPOINT
Dimension: XY
Bounding box: xmin: 2.711632 ymin: 4.302938 xmax: 13.5022 ymax: 13.86331
Geodetic CRS: WGS 84
# A tibble: 10 × 4
status_clean usage_capacity n geometry
* <chr> <dbl> <int> <MULTIPOINT [°]>
1 Functional 300 33687 ((3.064921 7.994882), (3.06…
2 Functional 1000 12124 ((3.080189 7.99252), (3.085…
3 Functional but needs repair 300 3306 ((3.340832 8.037962), (3.34…
4 Functional but needs repair 1000 1271 ((3.373801 7.992051), (3.33…
5 Functional but not in use 300 1071 ((3.046639 8.017765), (2.88…
6 Functional but not in use 1000 612 ((3.088655 8.005296), (3.05…
7 Functional 250 70 ((3.355785 6.498105), (3.67…
8 Functional but not in use 250 3 ((8.032945 6.878883), (7.00…
9 Functional 50 2 ((7.027967 4.765731), (8.92…
10 Functional but needs repair 250 2 ((6.465915 5.826699), (7.93…
-- reveal value :: “crucialness_score”
summary(wpt_functional$crucialness_score == 1) Mode FALSE TRUE
logical 47006 5142
-- determine the total population within 1 km by “crucialness_score”
freq(wpt_functional$crucialness_score == 1) n % val%
FALSE 47006 90.1 90.1
TRUE 5142 9.9 9.9
sum(wpt_functional[wpt_functional$crucialness_score == 1,]$local_population_1km)[1] 11252574
Remarks :
Given the “crucialness_score” is a ratio of current water point users to the total population within 1 km radius thereof :
Currently, 5,142 water points serve the population within a 1 km radius at its capacity limit.
The usage capacity may need to be increased to sustain or improve the growth or development rate within 1km of these water points.
Should the population within 1 km therefrom grow above 11,252,574, there may be multiple repercussions in resources management, urbanisation progress, local food and beverage consumption, local commodity prices, or worst case scenario would be the stability of civil society.
summary(wpt_functional$pressure_score > 1) Mode FALSE TRUE
logical 27469 24679
length(wpt_functional$pressure_score)[1] 52148
24679/52148*100 #percentage of functional waterpoints over their usage limit[1] 47.32492
Remarks :
Given the “pressure_score” is the ratio of the current water point users to the usage capacity thereof :
- 24,679, or 47.32% of functional water points, are currently over their limit of usage capacity.
3.4.5.3 Exploratory Data Analysis (EDA) :: wpt_functional
-- plot “status_clean”
ggplot(data = wpt_functional,
aes(fct_infreq(status_clean), fill=status_clean))+
geom_bar()+
geom_text(
aes(label=after_stat(count)),
stat='count',
nudge_x=-0.25,
vjust=-0.2)+
geom_text(
aes(label= scales::percent(signif(after_stat(count/sum(count))))),
stat='count',
nudge_x=0.25,
vjust=-0.2)+
scale_x_discrete(guide = guide_axis(n.dodge = 2))+
guides(fill=guide_legend (title = 'Status'))
-- plot “water_tech_category”
ggplot(data=wpt_functional,
aes(x=fct_infreq(
water_tech_category)))+
geom_bar(aes(
fill = water_tech_category),
width = 0.8)+
geom_text(aes(
label = ..count..),
stat = "count",
vjust=-0.2,
size = 3.5,
color = "black")+
scale_x_discrete(guide = guide_axis(n.dodge = 2))+
guides(fill=guide_legend (title = 'Water Tech Deployed'))Warning: The dot-dot notation (`..count..`) was deprecated in ggplot2 3.4.0.
ℹ Please use `after_stat(count)` instead.

-- plot “water_source_clean”
ggplot(data=wpt_functional,
aes(x=fct_infreq(
water_source_clean)))+
geom_bar(aes(
fill = water_source_clean),
width = 0.8)+
geom_text(aes(
label = ..count..),
stat = "count",
vjust=-0.2,
size = 3.5,
color = "black")+
scale_x_discrete(guide = guide_axis(
n.dodge = 2))+
guides(fill=guide_legend (
title = 'Source of Water Supply'))
3.4.5.4 add attribute to new data table
wp_nga <- bdy_nga %>%
mutate(`total_wp` = lengths(
st_intersects(bdy_nga, wp_joined1))) %>%
mutate(`wp_functional` = lengths(
st_intersects(bdy_nga, wpt_functional))) %>%
mutate(`pct_functional` = (`wp_functional`/`total_wp`*100))-- replace “NaN” with 0
wp_nga <- wp_nga %>%
mutate(`pct_functional` = replace_na(pct_functional, 0))
summary(wp_nga) shapeName geometry total_wp wp_functional
Length:774 MULTIPOLYGON :774 Min. : 0.0 Min. : 0.00
Class :character epsg:4326 : 0 1st Qu.: 45.0 1st Qu.: 17.00
Mode :character +proj=long...: 0 Median : 96.0 Median : 45.50
Mean :122.7 Mean : 67.36
3rd Qu.:168.8 3rd Qu.: 87.75
Max. :894.0 Max. :752.00
pct_functional
Min. : 0.00
1st Qu.: 32.61
Median : 47.41
Mean : 49.84
3rd Qu.: 66.99
Max. :100.00
3.4.5.5 extract non-functional water point
wpt_nonFunctional <- wp_joined1 %>%
filter(status_clean %in%
c("Abandoned/Decommissioned",
"Non-Functional",
"Non-Functional due to dry season"))-- save and read RDS file :: wpt_nonFuntional
write_rds(wpt_nonFunctional,"/jephOstan/ISSS624/class_project/project_2/data/geodata/wpt_nonFunctional.rds",compress = "xz")
wpt_nonFunctional <- read_rds("/jephOstan/ISSS624/class_project/project_2/data/geodata/wpt_nonFunctional.rds")3.4.5.6 inspect variable and value
-- reveal value :: “status_clean”
freq(wpt_nonFunctional$status_clean) n % val%
Abandoned/Decommissioned 234 0.7 0.7
Non-Functional 29385 91.7 91.7
Non-Functional due to dry season 2410 7.5 7.5
length(wpt_nonFunctional$row_id)[1] 32029
length(wpt_nonFunctional$row_id)/length(wp_joined1$row_id)*100[1] 33.7119
Remarks :
There are 32,204, which is about 33.9% out of total water points.
-- reveal value :: “usage_capacity”
freq(wpt_nonFunctional$usage_capacity) n % val%
250 41 0.1 0.1
300 20586 64.3 64.3
1000 11402 35.6 35.6
-- reveal value “usage_capacity” by “status_clean”
wpt_nonFunctional %>% count(status_clean, usage_capacity, sort = TRUE)Simple feature collection with 7 features and 3 fields
Geometry type: MULTIPOINT
Dimension: XY
Bounding box: xmin: 2.707441 ymin: 4.301812 xmax: 13.4192 ymax: 13.86567
Geodetic CRS: WGS 84
# A tibble: 7 × 4
status_clean usage_capac…¹ n geometry
* <chr> <dbl> <int> <MULTIPOINT [°]>
1 Non-Functional 300 18492 ((3.064526 7.994448), (3…
2 Non-Functional 1000 10852 ((3.083391 7.993231), (3…
3 Non-Functional due to dry season 300 2012 ((3.051752 7.984243), (3…
4 Non-Functional due to dry season 1000 398 ((3.056661 7.985808), (3…
5 Abandoned/Decommissioned 1000 152 ((4.713438 7.891137), (4…
6 Abandoned/Decommissioned 300 82 ((3.199483 8.912549), (2…
7 Non-Functional 250 41 ((3.976195 6.582998), (3…
# … with abbreviated variable name ¹usage_capacity
-- reveal “crucialness_score”
sum(wpt_nonFunctional$local_population_1km)[1] 93999535
sum(wpt_nonFunctional$water_point_population)[1] 46255888
Remarks :
Given the “crucialness_score” is a ratio of current water point users to the total population within a 1 km radius thereof , in the context of non-functional :
- Currently, out of 95,013,340 residents within a 1 km radius, there are 46,710,127 of them is affected by these non-functional water point.
3.4.5.7 EDA :: wpt_nonFunctional
-- plot “status_clean”
ggplot(data = wpt_nonFunctional,
aes(fct_infreq(status_clean),
fill=status_clean))+
geom_bar()+
geom_text(
aes(label=after_stat(count)),
stat='count',
nudge_x=-0.25,
vjust=-0.2)+
geom_text(
aes(label= scales::percent(
signif(
after_stat(
count/sum(count)
)))),
stat='count',
nudge_x=0.25,
vjust=-0.2)+
scale_x_discrete(
guide = guide_axis(
n.dodge = 2))+
guides(fill=guide_legend (
title = 'Status'))
-- plot “water_tech_category”
ggplot(data=wpt_nonFunctional,
aes(fct_infreq(
water_tech_category)))+
geom_bar(aes(
fill = water_tech_category),
width = 0.8)+
geom_text(aes(
label = ..count..),
stat = "count",
vjust=-0.2,
size = 3.5,
color = "black")+
scale_x_discrete(guide = guide_axis(
n.dodge = 2))+
guides(fill=guide_legend (
title = 'Water Tech Deployed'))
-- plot “water_source_clean”
ggplot(data=wpt_nonFunctional,
aes(fct_infreq(
water_source_clean)))+
geom_bar(aes(
fill = water_source_clean),
width = 0.8)+
geom_text(aes(
label = ..count..),
stat = "count",
vjust=-0.2,
size = 3.5,
color = "black")+
scale_x_discrete(guide = guide_axis(
n.dodge = 2))+
guides(fill=guide_legend (
title = 'Source of Water Supply'))
3.4.5.8 add wpt_nonFunctional to wp_nga
wp_nga <- wp_nga %>%
mutate(`wp_nonFunctional` = lengths(
st_intersects(bdy_nga, wpt_nonFunctional))) %>%
mutate(`pct_nonFunctional` = (`wp_nonFunctional`/`total_wp`*100))-- replace “NaN” with 0
wp_nga <- wp_nga %>%
mutate(`pct_nonFunctional` = replace_na(pct_nonFunctional, 0))
summary(wp_nga) shapeName geometry total_wp wp_functional
Length:774 MULTIPOLYGON :774 Min. : 0.0 Min. : 0.00
Class :character epsg:4326 : 0 1st Qu.: 45.0 1st Qu.: 17.00
Mode :character +proj=long...: 0 Median : 96.0 Median : 45.50
Mean :122.7 Mean : 67.36
3rd Qu.:168.8 3rd Qu.: 87.75
Max. :894.0 Max. :752.00
pct_functional wp_nonFunctional pct_nonFunctional
Min. : 0.00 Min. : 0.00 Min. : 0.00
1st Qu.: 32.61 1st Qu.: 12.00 1st Qu.: 20.77
Median : 47.41 Median : 33.50 Median : 34.89
Mean : 49.84 Mean : 41.37 Mean : 35.58
3rd Qu.: 66.99 3rd Qu.: 60.00 3rd Qu.: 50.00
Max. :100.00 Max. :278.00 Max. :100.00
3.4.5.9 extract unknown water point
wpt_unknown <- wp_joined1 %>%
filter(status_clean == "Unknown")-- save and read RDS file :: wpt_unknown
write_rds(wpt_unknown,"/jephOstan/ISSS624/class_project/project_2/data/geodata/wpt_unknown.rds")
wpt_unknown <- read_rds("/jephOstan/ISSS624/class_project/project_2/data/geodata/wpt_unknown.rds")3.4.5.10 inspect variable and value
-- reveal value :: “status_clean”
length(wpt_unknown$row_id)[1] 10656
length(wpt_unknown$row_id)/length(wp_joined1$row_id)*100[1] 11.2159
Remarks :
There are 10,656 water points with unknown status, about 11.22% of total water points.
-- determine affected population
sum(wpt_unknown$water_point_population)[1] 18831488
sum(wpt_unknown$local_population_1km)[1] 31418651
3.4.5.11 add wpt_unknown to wp_nga
wp_nga <- wp_nga %>%
mutate(`wp_unknown` = lengths(
st_intersects(bdy_nga, wpt_unknown))) %>%
mutate(`pct_unknown` = (`wp_unknown`/`total_wp`*100))-- replace “NaN” with 0
wp_nga <- wp_nga %>%
mutate(`pct_unknown` = replace_na(pct_unknown, 0))
summary(wp_nga) shapeName geometry total_wp wp_functional
Length:774 MULTIPOLYGON :774 Min. : 0.0 Min. : 0.00
Class :character epsg:4326 : 0 1st Qu.: 45.0 1st Qu.: 17.00
Mode :character +proj=long...: 0 Median : 96.0 Median : 45.50
Mean :122.7 Mean : 67.36
3rd Qu.:168.8 3rd Qu.: 87.75
Max. :894.0 Max. :752.00
pct_functional wp_nonFunctional pct_nonFunctional wp_unknown
Min. : 0.00 Min. : 0.00 Min. : 0.00 Min. : 0.00
1st Qu.: 32.61 1st Qu.: 12.00 1st Qu.: 20.77 1st Qu.: 0.00
Median : 47.41 Median : 33.50 Median : 34.89 Median : 0.00
Mean : 49.84 Mean : 41.37 Mean : 35.58 Mean : 13.76
3rd Qu.: 66.99 3rd Qu.: 60.00 3rd Qu.: 50.00 3rd Qu.: 17.75
Max. :100.00 Max. :278.00 Max. :100.00 Max. :219.00
pct_unknown
Min. : 0.00
1st Qu.: 0.00
Median : 0.00
Mean : 12.55
3rd Qu.: 20.83
Max. :100.00
3.4.5.12 visualise distribution :: “status_clean”
Usage of the code chunk below :
qtm( ) - tmap - to plot a thematic map quickly.
tmap_arrange( ) - tmap - to arrange small multiples in grid layout.
total_wp <- qtm(wp_nga,"total_wp")+
tm_layout(legend.height = 0.3, legend.width = 0.5)
wp_functional <- qtm(wp_nga,"wp_functional")+
tm_layout(legend.height = 0.3, legend.width = 0.5)
wp_nonFunctional <- qtm(wp_nga,"wp_nonFunctional")+
tm_layout(legend.height = 0.3, legend.width = 0.5)
wp_unknown <- qtm(wp_nga,"wp_unknown")+
tm_layout(legend.height = 0.3, legend.width = 0.5)
tmap_arrange(total_wp, wp_functional, wp_nonFunctional, wp_unknown, asp=0, ncol = 2, nrow = 2, widths = 5, heights = 10, sync = TRUE)
3.4.5.13 extract “water_tech_category” to wp_nga
freq(wp_joined1$water_tech_category, sort = "dec") n % val%
Hand Pump 58755 61.8 61.8
Mechanized Pump 25644 27.0 27.0
Unknown 10055 10.6 10.6
Tapstand 553 0.6 0.6
Rope and Bucket 1 0.0 0.0
Remarks :
Only “Hand Pump”, “Mechanized Pump”, and “Tapstand” are to be extracted for further analysis as the rest are either less than 0.5% or “Unknown”.
wtc_hPump <- wp_joined1 %>%
filter(water_tech_category %in%
"Hand Pump")
wtc_mPump <- wp_joined1 %>%
filter(water_tech_category %in%
"Mechanized Pump")
wtc_tStand <- wp_joined1 %>%
filter(water_tech_category %in%
"Tapstand")
wp_nga <- wp_nga %>%
mutate(`total_handPump` = lengths(
st_intersects(bdy_nga, wtc_hPump)
)) %>%
mutate(`total_mechPump` = lengths(
st_intersects(bdy_nga, wtc_mPump)
)) %>%
mutate(`total_tapStand` = lengths(
st_intersects(bdy_nga, wtc_tStand)
)) %>%
mutate(`pct_handPump` = (`total_handPump`/`total_wp`*100)) %>%
mutate(`pct_mechPump` = (`total_mechPump`/`total_wp`*100)) %>%
mutate(`pct_tapStand` = (`total_tapStand`/`total_wp`*100))-- replace “NaN” with 0
wp_nga <- wp_nga %>%
mutate(`pct_handPump` = replace_na(pct_handPump, 0)) %>%
mutate(`pct_mechPump` = replace_na(pct_mechPump, 0)) %>%
mutate(`pct_tapStand` = replace_na(pct_tapStand, 0))
summary(wp_nga) shapeName geometry total_wp wp_functional
Length:774 MULTIPOLYGON :774 Min. : 0.0 Min. : 0.00
Class :character epsg:4326 : 0 1st Qu.: 45.0 1st Qu.: 17.00
Mode :character +proj=long...: 0 Median : 96.0 Median : 45.50
Mean :122.7 Mean : 67.36
3rd Qu.:168.8 3rd Qu.: 87.75
Max. :894.0 Max. :752.00
pct_functional wp_nonFunctional pct_nonFunctional wp_unknown
Min. : 0.00 Min. : 0.00 Min. : 0.00 Min. : 0.00
1st Qu.: 32.61 1st Qu.: 12.00 1st Qu.: 20.77 1st Qu.: 0.00
Median : 47.41 Median : 33.50 Median : 34.89 Median : 0.00
Mean : 49.84 Mean : 41.37 Mean : 35.58 Mean : 13.76
3rd Qu.: 66.99 3rd Qu.: 60.00 3rd Qu.: 50.00 3rd Qu.: 17.75
Max. :100.00 Max. :278.00 Max. :100.00 Max. :219.00
pct_unknown total_handPump total_mechPump total_tapStand
Min. : 0.00 Min. : 0.00 Min. : 0.00 Min. : 0.0000
1st Qu.: 0.00 1st Qu.: 6.00 1st Qu.: 11.00 1st Qu.: 0.0000
Median : 0.00 Median : 47.00 Median : 25.50 Median : 0.0000
Mean : 12.55 Mean : 75.89 Mean : 33.12 Mean : 0.7145
3rd Qu.: 20.83 3rd Qu.:111.00 3rd Qu.: 46.00 3rd Qu.: 0.0000
Max. :100.00 Max. :764.00 Max. :245.00 Max. :42.0000
pct_handPump pct_mechPump pct_tapStand
Min. : 0.00 Min. : 0.00 Min. : 0.0000
1st Qu.: 16.70 1st Qu.: 12.20 1st Qu.: 0.0000
Median : 50.99 Median : 31.27 Median : 0.0000
Mean : 48.73 Mean : 37.54 Mean : 0.5794
3rd Qu.: 77.78 3rd Qu.: 57.71 3rd Qu.: 0.0000
Max. :100.00 Max. :100.00 Max. :32.8947
3.4.5.14 visualise wp_nga distribution :: “water_tech_category”
tmap_mode("view")tmap mode set to interactive viewing
handPump <- tm_shape(bdy_nga)+
tm_polygons(alpha = 0.1) +
tm_shape(wp_nga) +
tm_dots(col = "pct_handPump",
border.col = "gray60",
border.lwd = 0.5) +
tm_view(set.zoom.limits = c(5,9))
mechPump <- tm_shape(bdy_nga)+
tm_polygons(alpha = 0.1) +
tm_shape(wp_nga) +
tm_dots(col = "pct_mechPump",
border.col = "gray60",
border.lwd = 0.5) +
tm_view(set.zoom.limits = c(5,9))
tapStand <- tm_shape(bdy_nga)+
tm_polygons(alpha = 0.1) +
tm_shape(wp_nga) +
tm_dots(col = "pct_tapStand",
border.col = "gray60",
border.lwd = 0.5) +
tm_view(set.zoom.limits = c(5,9))
tmap_arrange(handPump, mechPump, tapStand,
asp=1,
ncol=2,
sync = TRUE)tmap_mode("plot")tmap mode set to plotting
3.4.5.15 extract “usage_capacity” to wp_nga
freq(wp_joined1$usage_capacity, sort = "dec") n % val%
300 68789 72.4 72.4
1000 25644 27.0 27.0
250 573 0.6 0.6
50 2 0.0 0.0
Remarks :
Only “300”, “1000”, and “250” are to be extracted for further analysis as the rest are either less than 0.5% or “Unknown”.
But, “50” will be included in the new variable “total_ucN1000” as part of the none ‘1000’ “usage_capacity” value.
uCap_300 <- wp_joined1 %>%
filter(usage_capacity %in%
"300")
uCap_1000 <- wp_joined1 %>%
filter(usage_capacity %in%
"1000")
uCap_250 <- wp_joined1 %>%
filter(usage_capacity %in%
"250")
uCap_50 <- wp_joined1 %>%
filter(usage_capacity %in%
"50")
wp_nga <- wp_nga %>%
mutate(`total_uc300` = lengths(
st_intersects(bdy_nga, uCap_300)
)) %>%
mutate(`total_uc1000` = lengths(
st_intersects(bdy_nga, uCap_1000)
)) %>%
mutate(`total_uc250` = lengths(
st_intersects(bdy_nga, uCap_250)
)) %>%
mutate(`total_uc50` = lengths(
st_intersects(bdy_nga, uCap_50)
)) %>%
mutate(`total_ucN1000` = ((lengths(
st_intersects(
bdy_nga, uCap_300))) + (lengths(
st_intersects(
bdy_nga, uCap_250))) + (lengths(
st_intersects(
bdy_nga, uCap_50))))
)%>%
mutate(`pct_ucN1000` = (`total_ucN1000`/`total_wp`*100)) %>%
mutate(`pct_uc300` = (`total_uc300`/`total_wp`*100)) %>%
mutate(`pct_uc1000` = (`total_uc1000`/`total_wp`*100)) %>%
mutate(`pct_uc250` = (`total_uc250`/`total_wp`*100))-- replace “NaN” with 0
wp_nga <- wp_nga %>%
mutate(`pct_ucN1000` = replace_na(pct_ucN1000, 0)) %>%
mutate(`pct_uc300` = replace_na(pct_uc300, 0)) %>%
mutate(`pct_uc1000` = replace_na(pct_uc1000, 0)) %>%
mutate(`pct_uc250` = replace_na(pct_uc250, 0))
summary(wp_nga) shapeName geometry total_wp wp_functional
Length:774 MULTIPOLYGON :774 Min. : 0.0 Min. : 0.00
Class :character epsg:4326 : 0 1st Qu.: 45.0 1st Qu.: 17.00
Mode :character +proj=long...: 0 Median : 96.0 Median : 45.50
Mean :122.7 Mean : 67.36
3rd Qu.:168.8 3rd Qu.: 87.75
Max. :894.0 Max. :752.00
pct_functional wp_nonFunctional pct_nonFunctional wp_unknown
Min. : 0.00 Min. : 0.00 Min. : 0.00 Min. : 0.00
1st Qu.: 32.61 1st Qu.: 12.00 1st Qu.: 20.77 1st Qu.: 0.00
Median : 47.41 Median : 33.50 Median : 34.89 Median : 0.00
Mean : 49.84 Mean : 41.37 Mean : 35.58 Mean : 13.76
3rd Qu.: 66.99 3rd Qu.: 60.00 3rd Qu.: 50.00 3rd Qu.: 17.75
Max. :100.00 Max. :278.00 Max. :100.00 Max. :219.00
pct_unknown total_handPump total_mechPump total_tapStand
Min. : 0.00 Min. : 0.00 Min. : 0.00 Min. : 0.0000
1st Qu.: 0.00 1st Qu.: 6.00 1st Qu.: 11.00 1st Qu.: 0.0000
Median : 0.00 Median : 47.00 Median : 25.50 Median : 0.0000
Mean : 12.55 Mean : 75.89 Mean : 33.12 Mean : 0.7145
3rd Qu.: 20.83 3rd Qu.:111.00 3rd Qu.: 46.00 3rd Qu.: 0.0000
Max. :100.00 Max. :764.00 Max. :245.00 Max. :42.0000
pct_handPump pct_mechPump pct_tapStand total_uc300
Min. : 0.00 Min. : 0.00 Min. : 0.0000 Min. : 0.00
1st Qu.: 16.70 1st Qu.: 12.20 1st Qu.: 0.0000 1st Qu.: 15.25
Median : 50.99 Median : 31.27 Median : 0.0000 Median : 59.00
Mean : 48.73 Mean : 37.54 Mean : 0.5794 Mean : 88.85
3rd Qu.: 77.78 3rd Qu.: 57.71 3rd Qu.: 0.0000 3rd Qu.:126.75
Max. :100.00 Max. :100.00 Max. :32.8947 Max. :767.00
total_uc1000 total_uc250 total_uc50 total_ucN1000
Min. : 0.00 Min. : 0.0000 Min. :0.000000 Min. : 0.00
1st Qu.: 11.00 1st Qu.: 0.0000 1st Qu.:0.000000 1st Qu.: 16.00
Median : 25.50 Median : 0.0000 Median :0.000000 Median : 60.00
Mean : 33.12 Mean : 0.7403 Mean :0.002584 Mean : 89.59
3rd Qu.: 46.00 3rd Qu.: 0.0000 3rd Qu.:0.000000 3rd Qu.:127.75
Max. :245.00 Max. :42.0000 Max. :1.000000 Max. :767.00
pct_ucN1000 pct_uc300 pct_uc1000 pct_uc250
Min. : 0.00 Min. : 0.00 Min. : 0.00 Min. : 0.0000
1st Qu.: 39.68 1st Qu.: 38.67 1st Qu.: 12.20 1st Qu.: 0.0000
Median : 67.03 Median : 65.91 Median : 31.27 Median : 0.0000
Mean : 60.78 Mean : 60.17 Mean : 37.54 Mean : 0.6114
3rd Qu.: 87.35 3rd Qu.: 87.02 3rd Qu.: 57.71 3rd Qu.: 0.0000
Max. :100.00 Max. :100.00 Max. :100.00 Max. :32.8947
3.4.5.16 visualise wp_nga distribution :: “usage_capacity”
tmap_mode("view")tmap mode set to interactive viewing
uc300 <- tm_shape(bdy_nga)+
tm_polygons(alpha = 0.1) +
tm_shape(wp_nga) +
tm_dots(col = "pct_uc300",
border.col = "gray60",
border.lwd = 0.5) +
tm_view(set.zoom.limits = c(5,9))
uc1000 <- tm_shape(bdy_nga)+
tm_polygons(alpha = 0.1) +
tm_shape(wp_nga) +
tm_dots(col = "pct_uc1000",
border.col = "gray60",
border.lwd = 0.5) +
tm_view(set.zoom.limits = c(5,9))
uc250 <- tm_shape(bdy_nga)+
tm_polygons(alpha = 0.1) +
tm_shape(wp_nga) +
tm_dots(col = "pct_uc250",
border.col = "gray60",
border.lwd = 0.5) +
tm_view(set.zoom.limits = c(5,9))
tmap_arrange(uc300, uc1000, uc250,
asp=1,
ncol=2,
sync = TRUE)tmap_mode("plot")tmap mode set to plotting
3.4.5.17 extract “is_urban” to wp_nga
urban_1 <- wp_joined1 %>%
filter(is_urban %in%
"TRUE")
urban_0 <- wp_joined1 %>%
filter(is_urban %in%
"FALSE")
wp_nga <- wp_nga %>%
mutate(`total_urban1` = lengths(
st_intersects(bdy_nga, urban_1)
)) %>%
mutate(`total_urban0` = lengths(
st_intersects(bdy_nga, urban_0)
)) %>%
mutate(`pct_urban1` = (`total_urban1`/`total_wp`*100)) %>%
mutate(`pct_urban0` = (`total_urban0`/`total_wp`*100))-- replace “NaN” with 0
wp_nga <- wp_nga %>%
mutate(`pct_urban1` = replace_na(pct_urban1, 0)) %>%
mutate(`pct_urban0` = replace_na(pct_urban0, 0))
summary(wp_nga) shapeName geometry total_wp wp_functional
Length:774 MULTIPOLYGON :774 Min. : 0.0 Min. : 0.00
Class :character epsg:4326 : 0 1st Qu.: 45.0 1st Qu.: 17.00
Mode :character +proj=long...: 0 Median : 96.0 Median : 45.50
Mean :122.7 Mean : 67.36
3rd Qu.:168.8 3rd Qu.: 87.75
Max. :894.0 Max. :752.00
pct_functional wp_nonFunctional pct_nonFunctional wp_unknown
Min. : 0.00 Min. : 0.00 Min. : 0.00 Min. : 0.00
1st Qu.: 32.61 1st Qu.: 12.00 1st Qu.: 20.77 1st Qu.: 0.00
Median : 47.41 Median : 33.50 Median : 34.89 Median : 0.00
Mean : 49.84 Mean : 41.37 Mean : 35.58 Mean : 13.76
3rd Qu.: 66.99 3rd Qu.: 60.00 3rd Qu.: 50.00 3rd Qu.: 17.75
Max. :100.00 Max. :278.00 Max. :100.00 Max. :219.00
pct_unknown total_handPump total_mechPump total_tapStand
Min. : 0.00 Min. : 0.00 Min. : 0.00 Min. : 0.0000
1st Qu.: 0.00 1st Qu.: 6.00 1st Qu.: 11.00 1st Qu.: 0.0000
Median : 0.00 Median : 47.00 Median : 25.50 Median : 0.0000
Mean : 12.55 Mean : 75.89 Mean : 33.12 Mean : 0.7145
3rd Qu.: 20.83 3rd Qu.:111.00 3rd Qu.: 46.00 3rd Qu.: 0.0000
Max. :100.00 Max. :764.00 Max. :245.00 Max. :42.0000
pct_handPump pct_mechPump pct_tapStand total_uc300
Min. : 0.00 Min. : 0.00 Min. : 0.0000 Min. : 0.00
1st Qu.: 16.70 1st Qu.: 12.20 1st Qu.: 0.0000 1st Qu.: 15.25
Median : 50.99 Median : 31.27 Median : 0.0000 Median : 59.00
Mean : 48.73 Mean : 37.54 Mean : 0.5794 Mean : 88.85
3rd Qu.: 77.78 3rd Qu.: 57.71 3rd Qu.: 0.0000 3rd Qu.:126.75
Max. :100.00 Max. :100.00 Max. :32.8947 Max. :767.00
total_uc1000 total_uc250 total_uc50 total_ucN1000
Min. : 0.00 Min. : 0.0000 Min. :0.000000 Min. : 0.00
1st Qu.: 11.00 1st Qu.: 0.0000 1st Qu.:0.000000 1st Qu.: 16.00
Median : 25.50 Median : 0.0000 Median :0.000000 Median : 60.00
Mean : 33.12 Mean : 0.7403 Mean :0.002584 Mean : 89.59
3rd Qu.: 46.00 3rd Qu.: 0.0000 3rd Qu.:0.000000 3rd Qu.:127.75
Max. :245.00 Max. :42.0000 Max. :1.000000 Max. :767.00
pct_ucN1000 pct_uc300 pct_uc1000 pct_uc250
Min. : 0.00 Min. : 0.00 Min. : 0.00 Min. : 0.0000
1st Qu.: 39.68 1st Qu.: 38.67 1st Qu.: 12.20 1st Qu.: 0.0000
Median : 67.03 Median : 65.91 Median : 31.27 Median : 0.0000
Mean : 60.78 Mean : 60.17 Mean : 37.54 Mean : 0.6114
3rd Qu.: 87.35 3rd Qu.: 87.02 3rd Qu.: 57.71 3rd Qu.: 0.0000
Max. :100.00 Max. :100.00 Max. :100.00 Max. :32.8947
total_urban1 total_urban0 pct_urban1 pct_urban0
Min. : 0.00 Min. : 0.00 Min. : 0.00 Min. : 0.00
1st Qu.: 0.00 1st Qu.: 23.00 1st Qu.: 0.00 1st Qu.: 57.27
Median : 9.00 Median : 64.00 Median : 11.95 Median : 86.45
Mean : 25.27 Mean : 97.45 Mean : 25.61 Mean : 72.71
3rd Qu.: 33.00 3rd Qu.:141.00 3rd Qu.: 38.44 3rd Qu.:100.00
Max. :324.00 Max. :894.00 Max. :100.00 Max. :100.00
3.4.5.18 visualise wp_nga distribution :: “is_urban”
tmap_mode("view")tmap mode set to interactive viewing
urban1 <- tm_shape(bdy_nga)+
tm_polygons(alpha = 0.1) +
tm_shape(wp_nga) +
tm_dots(col = "pct_urban1",
border.col = "gray60",
border.lwd = 0.5) +
tm_view(set.zoom.limits = c(5,9))
urban0 <- tm_shape(bdy_nga)+
tm_polygons(alpha = 0.1) +
tm_shape(wp_nga) +
tm_dots(col = "pct_urban0",
border.col = "gray60",
border.lwd = 0.5) +
tm_view(set.zoom.limits = c(5,9))
tmap_arrange(urban1, urban0,
asp=1,
ncol=2,
sync = TRUE)tmap_mode("plot")tmap mode set to plotting
3.4.5.19 save and read RDS File :: wp_nga
write_rds(wp_nga,"/jephOstan/ISSS624/class_project/project_2/data/geodata/wp_nga.rds")
wp_nga <- read_rds("/jephOstan/ISSS624/class_project/project_2/data/geodata/wp_nga.rds")3.4.5.20 transform to Projected Coordinate System
Usage of the code chunk below :
st_crs( ) - sf - to inspect the coordinate reference system.
st_crs(wp_nga)Coordinate Reference System:
User input: EPSG:4326
wkt:
GEOGCRS["WGS 84",
ENSEMBLE["World Geodetic System 1984 ensemble",
MEMBER["World Geodetic System 1984 (Transit)"],
MEMBER["World Geodetic System 1984 (G730)"],
MEMBER["World Geodetic System 1984 (G873)"],
MEMBER["World Geodetic System 1984 (G1150)"],
MEMBER["World Geodetic System 1984 (G1674)"],
MEMBER["World Geodetic System 1984 (G1762)"],
MEMBER["World Geodetic System 1984 (G2139)"],
ELLIPSOID["WGS 84",6378137,298.257223563,
LENGTHUNIT["metre",1]],
ENSEMBLEACCURACY[2.0]],
PRIMEM["Greenwich",0,
ANGLEUNIT["degree",0.0174532925199433]],
CS[ellipsoidal,2],
AXIS["geodetic latitude (Lat)",north,
ORDER[1],
ANGLEUNIT["degree",0.0174532925199433]],
AXIS["geodetic longitude (Lon)",east,
ORDER[2],
ANGLEUNIT["degree",0.0174532925199433]],
USAGE[
SCOPE["Horizontal component of 3D system."],
AREA["World."],
BBOX[-90,-180,90,180]],
ID["EPSG",4326]]
Remarks :
The EPSG for wp_nga is 4326, which is WGS 84. To compute the proximity distance matrix for clustering analysis, this coordinate reference system needs to transform into EPSG: 26391.
Usage of the code chunk below :
st_set_crs( ) - sf - to update the coordinate reference system.
wp_ngaTrans <- st_set_crs(wp_nga, 26391)Warning: st_crs<- : replacing crs does not reproject data; use st_transform for
that
bdy_ngaTrans <- st_set_crs(bdy_nga, 26391)Warning: st_crs<- : replacing crs does not reproject data; use st_transform for
that
-- review CRS :: wp_ngaTrans
st_crs(wp_ngaTrans)Coordinate Reference System:
User input: EPSG:26391
wkt:
PROJCRS["Minna / Nigeria West Belt",
BASEGEOGCRS["Minna",
DATUM["Minna",
ELLIPSOID["Clarke 1880 (RGS)",6378249.145,293.465,
LENGTHUNIT["metre",1]]],
PRIMEM["Greenwich",0,
ANGLEUNIT["degree",0.0174532925199433]],
ID["EPSG",4263]],
CONVERSION["Nigeria West Belt",
METHOD["Transverse Mercator",
ID["EPSG",9807]],
PARAMETER["Latitude of natural origin",4,
ANGLEUNIT["degree",0.0174532925199433],
ID["EPSG",8801]],
PARAMETER["Longitude of natural origin",4.5,
ANGLEUNIT["degree",0.0174532925199433],
ID["EPSG",8802]],
PARAMETER["Scale factor at natural origin",0.99975,
SCALEUNIT["unity",1],
ID["EPSG",8805]],
PARAMETER["False easting",230738.26,
LENGTHUNIT["metre",1],
ID["EPSG",8806]],
PARAMETER["False northing",0,
LENGTHUNIT["metre",1],
ID["EPSG",8807]]],
CS[Cartesian,2],
AXIS["(E)",east,
ORDER[1],
LENGTHUNIT["metre",1]],
AXIS["(N)",north,
ORDER[2],
LENGTHUNIT["metre",1]],
USAGE[
SCOPE["Engineering survey, topographic mapping."],
AREA["Nigeria - onshore west of 6°30'E, onshore and offshore shelf."],
BBOX[3.57,2.69,13.9,6.5]],
ID["EPSG",26391]]
-- review CRS :: bdy_ngaTrans
st_crs(bdy_ngaTrans)Coordinate Reference System:
User input: EPSG:26391
wkt:
PROJCRS["Minna / Nigeria West Belt",
BASEGEOGCRS["Minna",
DATUM["Minna",
ELLIPSOID["Clarke 1880 (RGS)",6378249.145,293.465,
LENGTHUNIT["metre",1]]],
PRIMEM["Greenwich",0,
ANGLEUNIT["degree",0.0174532925199433]],
ID["EPSG",4263]],
CONVERSION["Nigeria West Belt",
METHOD["Transverse Mercator",
ID["EPSG",9807]],
PARAMETER["Latitude of natural origin",4,
ANGLEUNIT["degree",0.0174532925199433],
ID["EPSG",8801]],
PARAMETER["Longitude of natural origin",4.5,
ANGLEUNIT["degree",0.0174532925199433],
ID["EPSG",8802]],
PARAMETER["Scale factor at natural origin",0.99975,
SCALEUNIT["unity",1],
ID["EPSG",8805]],
PARAMETER["False easting",230738.26,
LENGTHUNIT["metre",1],
ID["EPSG",8806]],
PARAMETER["False northing",0,
LENGTHUNIT["metre",1],
ID["EPSG",8807]]],
CS[Cartesian,2],
AXIS["(E)",east,
ORDER[1],
LENGTHUNIT["metre",1]],
AXIS["(N)",north,
ORDER[2],
LENGTHUNIT["metre",1]],
USAGE[
SCOPE["Engineering survey, topographic mapping."],
AREA["Nigeria - onshore west of 6°30'E, onshore and offshore shelf."],
BBOX[3.57,2.69,13.9,6.5]],
ID["EPSG",26391]]
3.5 Exploratory Data Analysis
3.5.1 Identify Outliers
3.5.1.1 plot boxplot “pct_functional”
ggplot(data=wp_ngaTrans,
aes(x=`pct_functional`)) +
geom_boxplot(color="black",
fill="#543005")
3.5.1.2 plot boxplot “pct_nonFunctional”
ggplot(data=wp_ngaTrans,
aes(x=`pct_nonFunctional`)) +
geom_boxplot(color="black",
fill="#C16622FF")
3.5.1.3 plot boxplot “pct_unknown”
ggplot(data=wp_ngaTrans,
aes(x=`pct_unknown`)) +
geom_boxplot(color="black",
fill="#FFA319FF")
Remarks :
Among these 3 key categories of “status_clean”, “unknown” has the most outliers.
3.5.2 Multi-plot Histogram
3.5.2.1 plot histogram for “status_clean”
pctFunctional <- ggplot(data = wp_ngaTrans,
aes(x = `pct_functional`))+
geom_histogram(bins=10,
colour = "black",
fill = "#543005")
pctNonFunctional <- ggplot(data = wp_ngaTrans,
aes(x = `pct_nonFunctional`))+
geom_histogram(bins=10,
colour = "black",
fill = "#C16622FF")
pctUnknown <- ggplot(data = wp_ngaTrans,
aes(x = `pct_unknown`))+
geom_histogram(bins = 10,
colour = "black",
fill = "#FFA319FF")ggarrange(pctFunctional,pctNonFunctional,pctUnknown,
ncol = 2,
nrow = 2)
4. CORRELATION ANALYSIS
4.1 Create Data Table for Correlation Matrix Analysis
cluster_vars <- wp_ngaTrans %>%
st_set_geometry(NULL) %>%
select("shapeName",
"pct_functional",
"pct_nonFunctional",
"pct_unknown",
"pct_handPump",
"pct_mechPump",
"pct_tapStand",
"pct_uc300",
"pct_uc1000",
"pct_ucN1000",
"pct_uc250",
"pct_urban0")
head(cluster_vars,5) shapeName pct_functional pct_nonFunctional pct_unknown pct_handPump
1 Aba North 41.17647 52.94118 5.882353 11.764706
2 Aba South 40.84507 46.47887 9.859155 9.859155
3 Abadam 0.00000 0.00000 0.000000 0.000000
4 Abaji 40.35088 59.64912 0.000000 40.350877
5 Abak 47.91667 50.00000 0.000000 8.333333
pct_mechPump pct_tapStand pct_uc300 pct_uc1000 pct_ucN1000 pct_uc250
1 82.35294 0 17.647059 82.35294 17.647059 0
2 87.32394 0 12.676056 87.32394 12.676056 0
3 0.00000 0 0.000000 0.00000 0.000000 0
4 59.64912 0 40.350877 59.64912 40.350877 0
5 91.66667 0 8.333333 91.66667 8.333333 0
pct_urban0
1 0.000000
2 5.633803
3 0.000000
4 84.210526
5 83.333333
4.2 Visualise Correlation Matrix
This plot allows to identify the pattern and the relationship in the matrix.
corrplot.mixed((cor(cluster_vars[,2:12])),
upper = "number",
lower = "ellipse",
tl.col = "black",
diag = "l",
tl.pos = "lt")
Remarks :
Following are the pairs with strong correlation :
| correlation coefficients | variable_1 | variable_2 |
|---|---|---|
| 1.00 | pct_mechPump | pct_uc1000 |
| 0.99 | pct_tapStand | pct_uc250 |
| 0.99 | pct_uc300 | pct_ucN1000 |
| -0.91 | pct_mechPump | pct_ucN1000 |
| -0.91 | pct_uc1000 | pct_ucN1000 |
| -0.90 | pct_mechPump | pct_uc300 |
| -0.90 | pct_uc300 | pct_uc1000 |
4.2.1 Replace Row ID with “shapeName”
row.names(cluster_vars) <- cluster_vars$shapeName
cluster_vars shapeName pct_functional pct_nonFunctional
Aba North Aba North 41.176471 52.9411765
Aba South Aba South 40.845070 46.4788732
Abadam Abadam 0.000000 0.0000000
Abaji Abaji 40.350877 59.6491228
Abak Abak 47.916667 50.0000000
Abakaliki Abakaliki 35.193133 18.0257511
Abeokuta North Abeokuta North 47.058824 44.1176471
Abeokuta South Abeokuta South 60.504202 27.7310924
Abi Abi 51.973684 39.4736842
Aboh-Mbaise Aboh-Mbaise 27.272727 37.8787879
Abua/Odual Abua/Odual 64.102564 33.3333333
Abuja Municipal Abuja Municipal 40.000000 54.0740741
Adavi Adavi 44.444444 55.5555556
Ado Ado 42.968750 27.3437500
Ado-Odo/Ota Ado-Odo/Ota 32.758621 41.9540230
Ado Ekiti Ado Ekiti 46.153846 43.1952663
Afijio Afijio 33.962264 34.9056604
Afikpo North Afikpo North 43.010753 41.3978495
Afikpo South Afikpo South 12.500000 50.0000000
Agaie Agaie 50.537634 49.4623656
Agatu Agatu 30.864198 66.6666667
Agege Agege 86.666667 6.6666667
Aguata Aguata 10.526316 15.7894737
Agwara Agwara 56.637168 41.5929204
Ahiazu-Mbaise Ahiazu-Mbaise 26.666667 42.6666667
Ahoada East Ahoada East 47.368421 52.6315789
Ahoada West Ahoada West 100.000000 0.0000000
Aiyedade Aiyedade 40.712468 39.4402036
Aiyedire Aiyedire 50.857143 32.5714286
Aiyekire (Gbonyin) Aiyekire (Gbonyin) 44.186047 34.8837209
Ajaokuta Ajaokuta 40.000000 60.0000000
Ajeromi-Ifelodun Ajeromi-Ifelodun 50.000000 12.5000000
Ajingi Ajingi 81.683168 18.3168317
Akamkpa Akamkpa 26.400000 37.6000000
Akinyele Akinyele 41.899441 34.0782123
Akko Akko 42.532468 56.4935065
Akoko-Edo Akoko-Edo 46.774194 50.0000000
Akoko North East Akoko North East 47.136564 52.4229075
Akoko North West Akoko North West 38.541667 59.3750000
Akoko South East Akoko South East 32.051282 67.9487179
Akoko South West Akoko South West 45.637584 54.3624161
Akpabuyo Akpabuyo 16.796875 52.7343750
Akuku Toru Akuku Toru 57.142857 28.5714286
Akure North Akure North 42.553191 57.4468085
Akure South Akure South 86.666667 13.3333333
Akwanga Akwanga 75.555556 23.3333333
Albasu Albasu 69.863014 30.1369863
Aleiro Aleiro 57.142857 41.9047619
Alimosho Alimosho 54.629630 22.5308642
Alkaleri Alkaleri 70.833333 29.1666667
Amuwo-Odofin Amuwo-Odofin 50.000000 20.0000000
Anambra East Anambra East 24.637681 23.1884058
Anambra West Anambra West 48.148148 31.4814815
Anaocha Anaocha 32.876712 19.1780822
Andoni Andoni 35.294118 64.7058824
Aninri Aninri 40.000000 0.0000000
Aniocha North Aniocha North 16.666667 66.6666667
Aniocha South Aniocha South 53.846154 23.0769231
Anka Anka 76.000000 24.0000000
Ankpa Ankpa 20.370370 74.0740741
Apa Apa 30.769231 61.5384615
Apapa Apapa 0.000000 0.0000000
Ardo-Kola Ardo-Kola 51.658768 31.7535545
Arewa-Dandi Arewa-Dandi 64.000000 36.0000000
Argungu Argungu 53.846154 46.1538462
Arochukwu Arochukwu 28.000000 28.0000000
Asa Asa 62.676056 29.5774648
Asari-Toru Asari-Toru 85.106383 10.6382979
Askira/Uba Askira/Uba 80.555556 19.4444444
Atakumosa East Atakumosa East 43.946188 41.2556054
Atakumosa West Atakumosa West 45.121951 41.8699187
Atiba Atiba 41.964286 25.8928571
Atigbo Atigbo 32.142857 45.5357143
Augie Augie 55.223881 44.7761194
Auyo Auyo 89.928058 10.0719424
Awe Awe 53.763441 43.0107527
Awgu Awgu 42.307692 30.7692308
Awka North Awka North 22.857143 0.0000000
Awka South Awka South 22.500000 22.5000000
Ayamelum Ayamelum 18.181818 57.5757576
Babura Babura 84.116331 15.8836689
Badagry Badagry 33.809524 48.0952381
Bade Bade 80.379747 19.6202532
Bagudo Bagudo 66.990291 33.0097087
Bagwai Bagwai 82.857143 17.1428571
Bakassi Bakassi 0.000000 0.0000000
Bakori Bakori 91.690544 8.3094556
Bakura Bakura 91.025641 7.6923077
Balanga Balanga 55.849057 44.1509434
Bali Bali 58.362989 29.1814947
Bama Bama 0.000000 0.0000000
Barikin Ladi Barikin Ladi 27.177700 34.8432056
Baruten Baruten 66.871166 28.2208589
Bassa Kogi Bassa Kogi 41.269841 58.7301587
Bassa Plateau Bassa Plateau 33.668342 25.1256281
Batagarawa Batagarawa 61.068702 38.9312977
Batsari Batsari 67.500000 32.5000000
Bauchi Bauchi 79.354839 20.6451613
Baure Baure 61.279461 38.7205387
Bayo Bayo 70.833333 29.1666667
Bebeji Bebeji 89.024390 10.9756098
Bekwara Bekwara 24.607330 40.3141361
Bende Bende 25.000000 25.0000000
Biase Biase 22.137405 44.2748092
Bichi Bichi 69.230769 30.7692308
Bida Bida 87.551867 12.4481328
Billiri Billiri 46.560847 52.3809524
Bindawa Bindawa 80.442804 19.5571956
Binji Binji 37.113402 62.8865979
Biriniwa Biriniwa 83.118557 16.8814433
Birni Kudu Birni Kudu 89.308176 10.6918239
Birnin-Gwari Birnin-Gwari 29.940120 70.0598802
Birnin Kebbi Birnin Kebbi 85.000000 13.3333333
Birnin Magaji Birnin Magaji 78.350515 21.6494845
Biu Biu 100.000000 0.0000000
Bodinga Bodinga 52.040816 47.9591837
Bogoro Bogoro 78.225806 20.9677419
Boki Boki 40.223464 35.7541899
Bokkos Bokkos 33.333333 25.0000000
Boluwaduro Boluwaduro 48.837209 39.5348837
Bomadi Bomadi 25.000000 75.0000000
Bonny Bonny 100.000000 0.0000000
Borgu Borgu 74.074074 23.1481481
Boripe Boripe 44.632768 46.8926554
Bosso Bosso 73.684211 21.8045113
Brass Brass 27.272727 68.1818182
Buji Buji 87.537994 12.4620061
Bukkuyum Bukkuyum 72.222222 27.7777778
Bungudu Bungudu 68.571429 31.4285714
Bunkure Bunkure 76.377953 23.6220472
Bunza Bunza 42.331288 57.0552147
Bursari Bursari 81.443299 18.5567010
Buruku Buruku 39.204545 39.2045455
Burutu Burutu 42.857143 28.5714286
Bwari Bwari 49.152542 49.1525424
Calabar-Municipal Calabar-Municipal 20.731707 31.7073171
Calabar South Calabar South 26.027397 64.3835616
Chanchaga Chanchaga 44.444444 55.5555556
Charanchi Charanchi 75.126904 24.8730964
Chibok Chibok 75.757576 24.2424242
Chikun Chikun 0.000000 100.0000000
Dala Dala 61.594203 38.4057971
Damaturu Damaturu 92.307692 7.6923077
Damban Damban 37.837838 62.1621622
Dambatta Dambatta 64.532020 35.4679803
Damboa Damboa 0.000000 0.0000000
Dan Musa Dan Musa 89.062500 10.9375000
Dandi Dandi 29.696970 70.3030303
Dandume Dandume 62.608696 37.3913043
Dange-Shuni Dange-Shuni 61.038961 38.9610390
Danja Danja 55.555556 44.4444444
Darazo Darazo 50.000000 50.0000000
Dass Dass 92.642140 7.3578595
Daura Daura 25.263158 74.7368421
Dawakin Kudu Dawakin Kudu 70.142180 29.3838863
Dawakin Tofa Dawakin Tofa 94.405594 5.5944056
Degema Degema 62.500000 25.0000000
Dekina Dekina 31.730769 67.3076923
Demsa Demsa 76.923077 23.0769231
Dikwa Dikwa 0.000000 0.0000000
Doguwa Doguwa 59.090909 40.9090909
Doma Doma 60.227273 38.6363636
Donga Donga 42.702703 44.8648649
Dukku Dukku 14.705882 85.2941176
Dunukofia Dunukofia 53.333333 35.5555556
Dutse Dutse 98.701299 1.2987013
Dutsi Dutsi 66.250000 33.7500000
Dutsin-Ma Dutsin-Ma 78.317152 21.6828479
Eastern Obolo Eastern Obolo 16.000000 80.0000000
Ebonyi Ebonyi 26.923077 15.3846154
Edati Edati 54.787234 44.6808511
Ede North Ede North 65.277778 22.6851852
Ede South Ede South 49.315068 26.7123288
Edu Edu 49.218750 50.7812500
Efon Efon 20.000000 51.1111111
Egbado North Egbado North 20.000000 38.3333333
Egbado South Egbado South 81.250000 6.2500000
Egbeda Egbeda 32.989691 35.0515464
Egbedore Egbedore 45.283019 30.1886792
Egor Egor 48.076923 51.9230769
Ehime-Mbano Ehime-Mbano 46.808511 48.9361702
Ejigbo Ejigbo 64.403292 26.3374486
Ekeremor Ekeremor 12.121212 87.8787879
Eket Eket 54.411765 45.5882353
Ekiti Ekiti 40.099010 59.9009901
Ekiti East Ekiti East 64.444444 4.4444444
Ekiti South West Ekiti South West 40.740741 48.1481481
Ekiti West Ekiti West 48.717949 35.0427350
Ekwusigo Ekwusigo 5.555556 8.3333333
Eleme Eleme 100.000000 0.0000000
Emohua Emohua 66.666667 33.3333333
Emure Emure 44.615385 27.6923077
Enugu East Enugu East 60.869565 17.3913043
Enugu North Enugu North 83.333333 4.1666667
Enugu South Enugu South 42.105263 5.2631579
Epe Epe 43.478261 41.0628019
Esan Central Esan Central 21.875000 62.5000000
Esan North East Esan North East 41.176471 58.8235294
Esan South East Esan South East 28.571429 71.4285714
Esan West Esan West 47.058824 52.9411765
Ese-Odo Ese-Odo 36.036036 63.0630631
Esit - Eket Esit - Eket 25.000000 73.6842105
Essien Udim Essien Udim 32.758621 63.7931034
Etche Etche 21.052632 47.3684211
Ethiope East Ethiope East 36.363636 45.4545455
Ethiope West Ethiope West 32.000000 64.0000000
Eti-Osa Eti-Osa 78.947368 0.0000000
Etim Ekpo Etim Ekpo 35.294118 64.7058824
Etinan Etinan 22.857143 77.1428571
Etsako Central Etsako Central 35.416667 60.4166667
Etsako East Etsako East 42.857143 54.7619048
Etsako West Etsako West 66.666667 33.3333333
Etung Etung 27.878788 49.0909091
Ewekoro Ewekoro 26.388889 18.0555556
Ezeagu Ezeagu 15.686275 19.6078431
Ezinihitte Ezinihitte 26.785714 33.9285714
Ezza North Ezza North 39.010989 19.7802198
Ezza South Ezza South 42.993631 19.1082803
Fagge Fagge 93.650794 6.3492063
Fakai Fakai 81.818182 18.1818182
Faskari Faskari 77.101449 22.8985507
Fika Fika 78.947368 21.0526316
Fufore Fufore 100.000000 0.0000000
Funakaye Funakaye 41.578947 58.4210526
Fune Fune 73.239437 26.7605634
Funtua Funtua 75.714286 24.2857143
Gabasawa Gabasawa 97.385621 2.6143791
Gada Gada 64.406780 35.5932203
Gagarawa Gagarawa 92.584270 7.4157303
Gamawa Gamawa 66.817156 33.1828442
Ganjuwa Ganjuwa 86.419753 13.5802469
Ganye Ganye 64.285714 35.7142857
Garki Garki 79.907621 20.0923788
Garko Garko 87.640449 12.3595506
Garum Mallam Garum Mallam 73.619632 26.3803681
Gashaka Gashaka 47.272727 38.7878788
Gassol Gassol 39.245283 39.2452830
Gaya Gaya 98.453608 1.5463918
Gbako Gbako 57.714286 42.2857143
Gboko Gboko 38.659794 37.1134021
Geidam Geidam 0.000000 0.0000000
Gezawa Gezawa 67.491166 32.5088339
Giade Giade 70.909091 28.1818182
Girei Girei 86.666667 13.3333333
Giwa Giwa 46.511628 53.4883721
Gokana Gokana 50.000000 50.0000000
Gombe Gombe 44.285714 54.2857143
Gombi Gombi 64.444444 33.3333333
Goronyo Goronyo 35.714286 64.2857143
Gubio Gubio 0.000000 0.0000000
Gudu Gudu 42.500000 57.5000000
Gujba Gujba 0.000000 0.0000000
Gulani Gulani 50.000000 50.0000000
Guma Guma 27.350427 52.1367521
Gumel Gumel 62.140992 37.8590078
Gummi Gummi 68.656716 31.3432836
Gurara Gurara 46.341463 28.0487805
Guri Guri 94.460641 5.5393586
Gusau Gusau 65.625000 34.3750000
Guyuk Guyuk 50.000000 45.4545455
Guzamala Guzamala 0.000000 0.0000000
Gwadabawa Gwadabawa 55.263158 44.7368421
Gwagwalada Gwagwalada 55.084746 43.2203390
Gwale Gwale 81.451613 18.5483871
Gwandu Gwandu 53.061224 46.9387755
Gwaram Gwaram 88.461538 11.5384615
Gwarzo Gwarzo 88.157895 11.8421053
Gwer East Gwer East 34.126984 60.3174603
Gwer West Gwer West 9.523810 85.7142857
Gwiwa Gwiwa 69.306931 30.6930693
Gwoza Gwoza 0.000000 0.0000000
Hadejia Hadejia 56.250000 43.7500000
Hawul Hawul 93.750000 6.2500000
Hong Hong 75.000000 25.0000000
Ibadan North Ibadan North 54.545455 12.1212121
Ibadan North East Ibadan North East 57.480315 35.4330709
Ibadan North West Ibadan North West 66.666667 28.7356322
Ibadan South East Ibadan South East 61.111111 16.6666667
Ibadan South West Ibadan South West 68.224299 23.3644860
Ibaji Ibaji 20.588235 79.4117647
Ibarapa Central Ibarapa Central 45.933014 33.4928230
Ibarapa East Ibarapa East 37.254902 34.3137255
Ibarapa North Ibarapa North 44.949495 33.3333333
Ibeju/Lekki Ibeju/Lekki 48.872180 32.3308271
Ibeno Ibeno 40.000000 60.0000000
Ibesikpo Asutan Ibesikpo Asutan 46.296296 51.8518519
Ibi Ibi 57.317073 29.2682927
Ibiono Ibom Ibiono Ibom 39.215686 60.7843137
Idah Idah 37.288136 61.0169492
Idanre Idanre 46.153846 52.8846154
Ideato North Ideato North 21.428571 14.2857143
Ideato South Ideato South 30.434783 26.0869565
Idemili North Idemili North 10.204082 4.0816327
Idemili South Idemili South 12.500000 7.1428571
Ido Ido 52.427184 28.1553398
Ido-Osi Ido-Osi 39.583333 6.2500000
Ifako-Ijaye Ifako-Ijaye 52.459016 31.1475410
Ife Central Ife Central 37.662338 59.7402597
Ife East Ife East 39.593909 51.7766497
Ife North Ife North 48.905109 43.7956204
Ife South Ife South 48.876404 41.0112360
Ifedayo Ifedayo 35.714286 41.6666667
Ifedore Ifedore 40.000000 60.0000000
Ifelodun Kwara Ifelodun Kwara 53.244592 46.2562396
Ifelodun Osun Ifelodun Osun 33.333333 60.3174603
Ifo Ifo 9.638554 12.0481928
Igabi Igabi 26.279863 73.7201365
Igalamela-Odolu Igalamela-Odolu 41.666667 54.1666667
Igbo-Etiti Igbo-Etiti 27.272727 27.2727273
Igbo-Eze North Igbo-Eze North 47.619048 7.1428571
Igbo-Eze South Igbo-Eze South 38.235294 8.8235294
Igueben Igueben 20.000000 80.0000000
Ihiala Ihiala 21.374046 22.1374046
Ihitte/Uboma Ihitte/Uboma 25.000000 47.2222222
Ijebu East Ijebu East 16.129032 6.4516129
Ijebu North Ijebu North 21.875000 53.1250000
Ijebu North East Ijebu North East 30.000000 45.5555556
Ijebu Ode Ijebu Ode 51.724138 27.5862069
Ijero Ijero 31.553398 47.5728155
Ijumu Ijumu 49.180328 50.8196721
Ika Ika 16.326531 83.6734694
Ika North East Ika North East 100.000000 0.0000000
Ika South Ika South 26.666667 73.3333333
Ikara Ikara 87.591241 12.4087591
Ikeduru Ikeduru 13.333333 33.3333333
Ikeja Ikeja 61.904762 4.7619048
Ikenne Ikenne 26.315789 21.0526316
Ikere Ikere 34.020619 55.6701031
Ikole Ikole 33.513514 36.7567568
Ikom Ikom 30.845771 42.7860697
Ikono Ikono 41.269841 58.7301587
Ikorodu Ikorodu 54.583333 26.2500000
Ikot Abasi Ikot Abasi 43.103448 56.8965517
Ikot Ekpene Ikot Ekpene 31.914894 68.0851064
Ikpoba-Okha Ikpoba-Okha 43.939394 56.0606061
Ikwerre Ikwerre 93.939394 6.0606061
Ikwo Ikwo 21.698113 14.7798742
Ikwuano Ikwuano 12.149533 40.1869159
Ila Ila 43.930636 38.1502890
Ilaje Ilaje 48.275862 51.7241379
Ile-Oluji-Okeigbo Ile-Oluji-Okeigbo 33.812950 65.4676259
Ilejemeji Ilejemeji 21.428571 54.7619048
Ilesha East Ilesha East 43.165468 38.8489209
Ilesha West Ilesha West 49.484536 46.3917526
Illela Illela 43.181818 56.8181818
Ilorin East Ilorin East 68.468468 31.5315315
Ilorin South Ilorin South 62.745098 36.2745098
Ilorin West Ilorin West 62.745098 37.2549020
Imeko-Afon Imeko-Afon 12.121212 0.0000000
Ingawa Ingawa 89.393939 10.6060606
Ini Ini 39.130435 60.8695652
Ipokia Ipokia 34.920635 39.6825397
Irele Irele 34.482759 65.5172414
Irepo Irepo 42.372881 32.2033898
Irepodun Kwara Irepodun Kwara 40.769231 58.4615385
Irepodun Osun Irepodun Osun 47.115385 40.3846154
Irepodun/Ifelodun Irepodun/Ifelodun 36.283186 26.5486726
Irewole Irewole 45.045045 45.0450450
Isa Isa 83.018868 16.9811321
Ise/Orun Ise/Orun 65.714286 2.8571429
Iseyin Iseyin 25.471698 11.3207547
Ishielu Ishielu 29.918033 34.4262295
Isi-Uzo Isi-Uzo 1.724138 74.1379310
Isiala-Ngwa North Isiala-Ngwa North 23.595506 34.8314607
Isiala-Ngwa South Isiala-Ngwa South 23.437500 51.5625000
Isiala Mbano Isiala Mbano 42.105263 44.7368421
Isin Isin 60.645161 36.1290323
Isiukwuato Isiukwuato 39.344262 27.8688525
Isokan Isokan 40.650407 39.8373984
Isoko North Isoko North 38.461538 61.5384615
Isoko South Isoko South 43.750000 56.2500000
Isu Isu 29.166667 8.3333333
Itas/Gadau Itas/Gadau 72.027972 27.9720280
Itesiwaju Itesiwaju 56.338028 9.8591549
Itu Itu 23.333333 75.0000000
Ivo Ivo 15.909091 38.6363636
Iwajowa Iwajowa 47.450980 31.3725490
Iwo Iwo 53.125000 26.2500000
Izzi Izzi 22.257053 9.0909091
Jaba Jaba 39.215686 58.8235294
Jada Jada 57.142857 42.8571429
Jahun Jahun 85.984848 14.0151515
Jakusko Jakusko 88.571429 11.4285714
Jalingo Jalingo 47.101449 42.0289855
Jama'are Jama'are 70.634921 29.3650794
Jega Jega 52.027027 47.9729730
Jema'a Jema'a 46.757679 53.2423208
Jere Jere 50.000000 24.3421053
Jibia Jibia 66.315789 33.6842105
Jos East Jos East 31.840796 26.3681592
Jos North Jos North 51.538462 20.7692308
Jos South Jos South 23.529412 45.4545455
Kabba/Bunu Kabba/Bunu 47.945205 52.0547945
Kabo Kabo 81.250000 18.7500000
Kachia Kachia 61.057692 38.9423077
Kaduna North Kaduna North 39.655172 60.3448276
Kaduna South Kaduna South 95.270270 4.7297297
Kafin Hausa Kafin Hausa 99.593496 0.4065041
Kafur Kafur 79.674797 20.3252033
Kaga Kaga 0.000000 0.0000000
Kagarko Kagarko 30.810811 69.1891892
Kaiama Kaiama 55.339806 39.8058252
Kaita Kaita 66.777409 33.2225914
Kajola Kajola 47.191011 30.8988764
Kajuru Kajuru 96.503497 3.1468531
Kala/Balge Kala/Balge 0.000000 0.0000000
Kalgo Kalgo 32.380952 67.6190476
Kaltungo Kaltungo 73.000000 26.6666667
Kanam Kanam 32.068966 25.8620690
Kankara Kankara 81.102362 18.8976378
Kanke Kanke 48.437500 19.8660714
Kankia Kankia 74.100719 25.8992806
Kano Municipal Kano Municipal 77.155172 22.8448276
Karasuwa Karasuwa 79.411765 20.5882353
Karaye Karaye 57.522124 42.4778761
Karim-Lamido Karim-Lamido 37.037037 44.4444444
Karu Karu 55.617978 36.5168539
Katagum Katagum 67.796610 32.2033898
Katcha Katcha 47.826087 34.7826087
Katsina Katsina 65.909091 34.0909091
Katsina-Ala Katsina-Ala 32.911392 33.5443038
Kaugama Kaugama 94.373866 5.6261343
Kaura Kaura 58.156028 41.8439716
Kaura Namoda Kaura Namoda 80.000000 20.0000000
Kauru Kauru 68.786127 30.6358382
Kazaure Kazaure 71.801567 28.1984334
Keana Keana 48.051948 51.9480519
Kebbe Kebbe 54.687500 45.3125000
Keffi Keffi 77.922078 20.7792208
Khana Khana 51.111111 42.2222222
Kibiya Kibiya 80.751174 19.2488263
Kirfi Kirfi 45.977011 43.6781609
Kiri Kasamma Kiri Kasamma 79.959920 20.0400802
Kiru Kiru 76.211454 22.4669604
Kiyawa Kiyawa 83.333333 16.6666667
Kogi Kogi 40.000000 60.0000000
Koko/Besse Koko/Besse 33.108108 66.8918919
Kokona Kokona 57.142857 41.9047619
Kolokuma/Opokuma Kolokuma/Opokuma 90.000000 5.0000000
Konduga Konduga 66.666667 0.0000000
Konshisha Konshisha 46.537396 24.0997230
Kontagora Kontagora 59.259259 38.2716049
Kosofe Kosofe 75.862069 10.3448276
Kubau Kubau 92.083333 7.9166667
Kudan Kudan 43.506494 56.4935065
Kuje Kuje 39.860140 51.7482517
Kukawa Kukawa 0.000000 0.0000000
Kumbotso Kumbotso 82.352941 17.6470588
Kunchi Kunchi 89.062500 10.9375000
Kura Kura 67.175573 32.8244275
Kurfi Kurfi 66.990291 33.0097087
Kurmi Kurmi 42.857143 41.8367347
Kusada Kusada 97.058824 2.9411765
Kwali Kwali 57.865169 41.0112360
Kwami Kwami 34.666667 65.3333333
Kwande Kwande 22.988506 64.3678161
Kware Kware 62.595420 36.6412214
Kwaya Kusar Kwaya Kusar 66.666667 33.3333333
Lafia Lafia 61.254613 34.6863469
Lagelu Lagelu 42.400000 40.8000000
Lagos Island Lagos Island 70.270270 29.7297297
Lagos Mainland Lagos Mainland 69.047619 4.7619048
Lamurde Lamurde 100.000000 0.0000000
Langtang North Langtang North 41.066667 24.8000000
Langtang South Langtang South 19.565217 44.5652174
Lapai Lapai 76.271186 22.8813559
Lau Lau 42.045455 43.1818182
Lavun Lavun 65.000000 34.0000000
Lere Lere 60.326087 39.6739130
Logo Logo 27.210884 41.4965986
Lokoja Lokoja 71.929825 28.0701754
Machina Machina 81.081081 18.9189189
Madagali Madagali 0.000000 0.0000000
Madobi Madobi 86.857143 13.1428571
Mafa Mafa 0.000000 0.0000000
Magama Magama 51.000000 44.0000000
Magumeri Magumeri 77.777778 22.2222222
Mai'adua Mai'adua 55.776892 44.2231076
Maiduguri Maiduguri 65.413534 3.0075188
Maigatari Maigatari 75.294118 24.7058824
Maiha Maiha 76.470588 23.5294118
Maiyama Maiyama 46.000000 54.0000000
Makoda Makoda 98.113208 1.8867925
Makurdi Makurdi 56.521739 40.2173913
Malam Madori Malam Madori 93.650794 6.3492063
Malumfashi Malumfashi 82.291667 17.7083333
Mangu Mangu 34.810127 31.3291139
Mani Mani 60.689655 39.3103448
Maradun Maradun 69.791667 30.2083333
Mariga Mariga 48.305085 50.8474576
Markafi Markafi 97.368421 1.7543860
Marte Marte 0.000000 0.0000000
Maru Maru 70.658683 29.3413174
Mashegu Mashegu 32.142857 64.2857143
Mashi Mashi 51.639344 48.3606557
Matazu Matazu 75.129534 24.8704663
Mayo-Belwa Mayo-Belwa 75.000000 25.0000000
Mbaitoli Mbaitoli 23.636364 23.6363636
Mbo Mbo 21.428571 78.5714286
Michika Michika 75.000000 25.0000000
Miga Miga 98.437500 1.5625000
Mikang Mikang 33.898305 31.5254237
Minjibir Minjibir 99.029126 0.9708738
Misau Misau 70.992366 29.0076336
Mkpat Enin Mkpat Enin 33.913043 66.0869565
Moba Moba 19.736842 42.1052632
Mobbar Mobbar 0.000000 0.0000000
Mokwa Mokwa 60.526316 39.4736842
Monguno Monguno 0.000000 0.0000000
Mopa-Muro Mopa-Muro 32.558140 67.4418605
Moro Moro 57.714286 37.1428571
Mubi North Mubi North 0.000000 100.0000000
Mubi South Mubi South 0.000000 100.0000000
Musawa Musawa 85.314685 14.6853147
Mushin Mushin 75.000000 15.6250000
Muya Muya 55.789474 44.2105263
Nafada Nafada 46.621622 53.3783784
Nangere Nangere 72.972973 27.0270270
Nasarawa Kano Nasarawa Kano 51.000000 49.0000000
Nasarawa Nasarawa 48.275862 45.5172414
Nasarawa-Eggon Nasarawa-Eggon 53.459119 44.6540881
Ndokwa East Ndokwa East 44.578313 54.2168675
Ndokwa West Ndokwa West 39.130435 56.5217391
Nembe Nembe 37.209302 62.7906977
Ngala Ngala 0.000000 0.0000000
Nganzai Nganzai 0.000000 0.0000000
Ngaski Ngaski 51.304348 48.6956522
Ngor-Okpala Ngor-Okpala 33.823529 17.6470588
Nguru Nguru 91.216216 8.7837838
Ningi Ningi 81.521739 18.4782609
Njaba Njaba 6.666667 46.6666667
Njikoka Njikoka 32.352941 38.2352941
Nkanu East Nkanu East 37.037037 1.8518519
Nkanu West Nkanu West 21.518987 45.5696203
Nkwerre Nkwerre 42.424242 54.5454545
Nnewi North Nnewi North 30.769231 17.9487179
Nnewi South Nnewi South 3.030303 6.0606061
Nsit Atai Nsit Atai 23.529412 75.0000000
Nsit Ibom Nsit Ibom 46.938776 53.0612245
Nsit Ubium Nsit Ubium 43.181818 56.8181818
Nsukka Nsukka 19.642857 17.8571429
Numan Numan 100.000000 0.0000000
Nwangele Nwangele 14.285714 60.7142857
Obafemi-Owode Obafemi-Owode 36.363636 22.7272727
Obanliku Obanliku 34.615385 36.2637363
Nasarawa Nasarawa Nasarawa Nasarawa 49.397590 28.9156627
Obi Nasarawa Obi Nasarawa 27.272727 72.7272727
Obi Ngwa Obi Ngwa 17.341040 64.7398844
Obia/Akpor Obia/Akpor 64.800000 34.4000000
Obokun Obokun 53.960396 41.0891089
Obot Akara Obot Akara 23.809524 63.4920635
Obowo Obowo 21.212121 37.8787879
Obubra Obubra 33.905579 49.3562232
Obudu Obudu 36.820084 38.4937238
Odeda Odeda 15.929204 32.7433628
Odigbo Odigbo 22.674419 77.3255814
Odo-Otin Odo-Otin 45.723684 38.8157895
Odogbolu Odogbolu 51.948052 28.5714286
Odukpani Odukpani 40.119760 54.4910180
Offa Offa 43.617021 56.3829787
Ofu Ofu 32.352941 67.6470588
Ogba/Egbema/Ndoni Ogba/Egbema/Ndoni 56.451613 19.3548387
Ogbadibo Ogbadibo 37.037037 41.9753086
Ogbaru Ogbaru 43.478261 26.0869565
Ogbia Ogbia 52.500000 45.0000000
Ogbomosho North Ogbomosho North 69.000000 28.0000000
Ogbomosho South Ogbomosho South 49.137931 50.0000000
Ogo Oluwa Ogo Oluwa 54.301075 41.3978495
Ogoja Ogoja 29.304029 32.6007326
Ogori/Magongo Ogori/Magongo 47.916667 52.0833333
Ogu/Bolo Ogu/Bolo 100.000000 0.0000000
Ogun waterside Ogun waterside 56.756757 39.1891892
Oguta Oguta 16.161616 59.5959596
Ohafia Ohafia 21.052632 50.0000000
Ohaji/Egbema Ohaji/Egbema 58.426966 28.6516854
Ohaozara Ohaozara 38.545455 25.8181818
Ohaukwu Ohaukwu 36.694678 23.5294118
Ohimini Ohimini 41.538462 58.4615385
Oji-River Oji-River 13.157895 34.2105263
Ojo Ojo 17.241379 36.7816092
Oju Oju 87.545788 10.9890110
Oke-Ero Oke-Ero 40.579710 58.4541063
Okehi Okehi 27.027027 72.9729730
Okene Okene 51.000000 49.0000000
Okigwe Okigwe 31.707317 58.5365854
Okitipupa Okitipupa 29.333333 70.6666667
Okobo Okobo 33.783784 63.5135135
Okpe Okpe 30.769231 38.4615385
Okpokwu Okpokwu 33.898305 52.5423729
Okrika Okrika 91.666667 8.3333333
Ola-oluwa Ola-oluwa 51.351351 9.4594595
Olamabolo Olamabolo 34.782609 65.2173913
Olorunda Olorunda 51.381215 36.4640884
Olorunsogo Olorunsogo 32.323232 31.3131313
Oluyole Oluyole 40.540541 22.9729730
Omala Omala 13.513514 86.4864865
Omumma Omumma 76.000000 24.0000000
Ona-Ara Ona-Ara 25.438596 51.7543860
Ondo East Ondo East 35.555556 64.4444444
Ondo West Ondo West 34.161491 65.2173913
Onicha Onicha 45.433790 22.6027397
Onitsha North Onitsha North 7.692308 7.6923077
Onitsha South Onitsha South 41.666667 16.6666667
Onna Onna 42.857143 57.1428571
Opobo/Nkoro Opobo/Nkoro 54.545455 45.4545455
Oredo Oredo 56.521739 43.4782609
Orelope Orelope 24.489796 39.7959184
Orhionmwon Orhionmwon 39.639640 60.3603604
Ori Ire Ori Ire 42.677824 15.0627615
Oriade Oriade 43.243243 38.6100386
Orlu Orlu 37.254902 27.4509804
Orolu Orolu 36.666667 55.8333333
Oron Oron 60.000000 40.0000000
Orsu Orsu 45.000000 35.0000000
Oru East Oru East 15.384615 23.0769231
Oru West Oru West 19.512195 21.9512195
Oruk Anam Oruk Anam 16.949153 83.0508475
Orumba North Orumba North 19.696970 21.2121212
Orumba South Orumba South 18.181818 33.3333333
Ose Ose 30.000000 69.3333333
Oshimili North Oshimili North 84.848485 15.1515152
Oshimili South Oshimili South 47.826087 47.8260870
Oshodi-Isolo Oshodi-Isolo 68.085106 0.0000000
Osisioma Ngwa Osisioma Ngwa 15.909091 36.3636364
Osogbo Osogbo 52.906977 36.6279070
Oturkpo Oturkpo 32.258065 54.8387097
Ovia North East Ovia North East 51.807229 39.7590361
Ovia South West Ovia South West 54.545455 38.9610390
Owan East Owan East 52.380952 46.0317460
Owan West Owan West 22.727273 77.2727273
Owerri-Municipal Owerri-Municipal 64.516129 6.4516129
Owerri North Owerri North 30.882353 44.1176471
Owerri West Owerri West 47.540984 32.7868852
Owo Owo 49.171271 49.1712707
Oye Oye 33.802817 50.7042254
Oyi Oyi 23.287671 36.9863014
Oyigbo Oyigbo 88.235294 11.7647059
Oyo East Oyo East 39.772727 34.0909091
Oyo West Oyo West 28.125000 18.7500000
Oyun Oyun 45.373134 54.6268657
Paikoro Paikoro 55.319149 43.6170213
Pankshin Pankshin 31.909548 25.1256281
Patani Patani 18.181818 81.8181818
Pategi Pategi 56.000000 40.7272727
Port-Harcourt Port-Harcourt 88.695652 11.3043478
Potiskum Potiskum 91.666667 8.3333333
Qua'an Pan Qua'an Pan 31.594203 31.5942029
Rabah Rabah 92.063492 7.9365079
Rafi Rafi 68.939394 31.0606061
Rano Rano 77.000000 23.0000000
Remo North Remo North 27.272727 43.1818182
Rijau Rijau 42.384106 50.3311258
Rimi Rimi 60.439560 39.5604396
Rimin Gado Rimin Gado 96.987952 3.0120482
Ringim Ringim 96.527778 3.4722222
Riyom Riyom 25.000000 33.5526316
Rogo Rogo 61.621622 38.3783784
Roni Roni 65.333333 34.6666667
Sabon-Gari Sabon-Gari 55.303030 44.6969697
Sabon Birni Sabon Birni 36.363636 63.6363636
Sabuwa Sabuwa 23.255814 76.7441860
Safana Safana 86.335404 13.6645963
Sagbama Sagbama 29.032258 66.1290323
Sakaba Sakaba 52.413793 47.5862069
Saki East Saki East 49.541284 36.6972477
Saki West Saki West 43.918919 33.1081081
Sandamu Sandamu 60.550459 39.4495413
Sanga Sanga 51.298701 48.7012987
Sapele Sapele 33.333333 58.3333333
Sardauna Sardauna 28.000000 56.0000000
Shagamu Shagamu 13.934426 50.8196721
Shagari Shagari 67.307692 30.7692308
Shanga Shanga 29.126214 70.8737864
Shani Shani 100.000000 0.0000000
Shanono Shanono 72.289157 27.7108434
Shelleng Shelleng 55.555556 44.4444444
Shendam Shendam 26.401869 25.4672897
Shinkafi Shinkafi 75.159236 24.8407643
Shira Shira 75.609756 24.3902439
Shiroro Shiroro 66.055046 22.9357798
Shomgom Shomgom 55.378486 44.6215139
Shomolu Shomolu 57.142857 21.4285714
Silame Silame 75.000000 25.0000000
Soba Soba 61.743341 38.2566586
Sokoto North Sokoto North 90.909091 9.0909091
Sokoto South Sokoto South 76.000000 20.0000000
Song Song 75.000000 25.0000000
Southern Ijaw Southern Ijaw 22.580645 74.1935484
Sule-Tankarkar Sule-Tankarkar 86.597938 13.4020619
Suleja Suleja 70.731707 29.2682927
Sumaila Sumaila 74.007220 25.9927798
Suru Suru 21.666667 78.3333333
Obi Benue Obi Benue 80.327869 9.8360656
Surulere Lagos Surulere Lagos 48.148148 47.6190476
Tafa Tafa 87.500000 12.5000000
Tafawa-Balewa Tafawa-Balewa 82.031250 16.4062500
Tai Tai 33.333333 66.6666667
Takai Takai 78.876404 21.1235955
Takum Takum 45.555556 46.6666667
Talata Mafara Talata Mafara 63.101604 36.8983957
Tambuwal Tambuwal 44.943820 55.0561798
Tangaza Tangaza 35.937500 64.0625000
Tarauni Tarauni 91.044776 8.9552239
Tarka Tarka 57.589286 22.7678571
Tarmua Tarmua 86.111111 13.8888889
Taura Taura 88.872180 11.1278195
Tofa Tofa 99.099099 0.9009009
Toro Toro 75.939850 21.0526316
Toto Toto 58.974359 38.4615385
Toungo Toungo 80.000000 20.0000000
Tsafe Tsafe 77.889447 22.1105528
Tsanyawa Tsanyawa 72.903226 27.0967742
Tudun Wada Tudun Wada 55.621302 43.7869822
Tureta Tureta 39.639640 59.4594595
Udenu Udenu 22.727273 4.5454545
Udi Udi 22.857143 9.5238095
Udu Udu 63.636364 36.3636364
Udung Uko Udung Uko 22.222222 77.7777778
Ughelli North Ughelli North 58.490566 33.9622642
Ughelli South Ughelli South 44.000000 56.0000000
Ugwunagbo Ugwunagbo 14.772727 46.5909091
Uhunmwonde Uhunmwonde 52.380952 44.4444444
Ukanafun Ukanafun 19.047619 80.9523810
Ukum Ukum 27.215190 46.2025316
Ukwa East Ukwa East 24.444444 66.6666667
Ukwa West Ukwa West 26.612903 52.4193548
Ukwuani Ukwuani 25.925926 72.2222222
Umu-Nneochi Umu-Nneochi 11.363636 29.5454545
Umuahia North Umuahia North 41.891892 22.9729730
Umuahia South Umuahia South 16.504854 39.8058252
Ungogo Ungogo 91.390728 8.6092715
Unuimo Unuimo 16.666667 33.3333333
Uruan Uruan 16.666667 83.3333333
Urue-Offong/Oruko Urue-Offong/Oruko 28.571429 71.4285714
Ushongo Ushongo 27.510917 42.7947598
Ussa Ussa 47.027027 41.6216216
Uvwie Uvwie 27.272727 72.7272727
Uyo Uyo 43.750000 56.2500000
Uzo-Uwani Uzo-Uwani 8.000000 4.0000000
Vandeikya Vandeikya 34.523810 31.5476190
Wamako Wamako 53.086420 46.9135802
Wamba Wamba 55.882353 43.1372549
Warawa Warawa 72.864322 27.1356784
Warji Warji 87.272727 12.7272727
Warri North Warri North 23.333333 73.3333333
Warri South Warri South 48.076923 51.9230769
Warri South West Warri South West 14.285714 85.7142857
Wasagu/Danko Wasagu/Danko 62.285714 37.7142857
Wase Wase 37.640449 28.0898876
Wudil Wudil 74.305556 25.6944444
Wukari Wukari 41.975309 38.6831276
Wurno Wurno 62.711864 37.2881356
Wushishi Wushishi 51.798561 48.2014388
Yabo Yabo 37.804878 62.1951220
Yagba East Yagba East 28.813559 71.1864407
Yagba West Yagba West 34.453782 65.5462185
Yakurr Yakurr 51.692308 37.8461538
Yala Yala 25.958702 31.8584071
Yamaltu/Deba Yamaltu/Deba 49.193548 50.8064516
Yankwashi Yankwashi 84.070796 15.9292035
Yauri Yauri 46.153846 53.8461538
Yenegoa Yenegoa 44.444444 55.5555556
Yola North Yola North 88.461538 11.5384615
Yola South Yola South 46.153846 53.8461538
Yorro Yorro 59.296482 24.6231156
Yunusari Yunusari 100.000000 0.0000000
Yusufari Yusufari 88.095238 11.9047619
Zaki Zaki 87.706856 12.2931442
Zango Zango 80.373832 19.6261682
Zango-Kataf Zango-Kataf 95.115681 4.3701799
Zaria Zaria 52.664577 47.3354232
Zing Zing 55.263158 33.6842105
Zurmi Zurmi 65.891473 34.1085271
Zuru Zuru 61.428571 38.5714286
pct_unknown pct_handPump pct_mechPump pct_tapStand
Aba North 5.8823529 11.7647059 82.3529412 0.0000000
Aba South 9.8591549 9.8591549 87.3239437 0.0000000
Abadam 0.0000000 0.0000000 0.0000000 0.0000000
Abaji 0.0000000 40.3508772 59.6491228 0.0000000
Abak 0.0000000 8.3333333 91.6666667 0.0000000
Abakaliki 46.7811159 43.7768240 9.4420601 15.4506438
Abeokuta North 8.8235294 14.7058824 76.4705882 0.0000000
Abeokuta South 11.7647059 16.8067227 70.5882353 0.0000000
Abi 7.2368421 59.8684211 32.8947368 0.0000000
Aboh-Mbaise 33.3333333 1.5151515 65.1515152 0.0000000
Abua/Odual 2.5641026 30.7692308 66.6666667 0.0000000
Abuja Municipal 5.9259259 34.0740741 59.2592593 0.0000000
Adavi 0.0000000 46.0317460 53.9682540 0.0000000
Ado 28.9062500 67.1875000 3.1250000 0.7812500
Ado-Odo/Ota 25.2873563 9.1954023 65.5172414 0.0000000
Ado Ekiti 10.6508876 53.8461538 33.1360947 6.5088757
Afijio 31.1320755 30.1886792 38.6792453 0.0000000
Afikpo North 15.5913978 52.6881720 31.7204301 1.0752688
Afikpo South 37.5000000 43.7500000 18.7500000 3.1250000
Agaie 0.0000000 53.7634409 46.2365591 0.0000000
Agatu 2.4691358 88.8888889 8.6419753 0.0000000
Agege 6.6666667 0.0000000 91.6666667 0.0000000
Aguata 73.6842105 0.0000000 23.6842105 0.0000000
Agwara 1.7699115 84.9557522 13.2743363 0.0000000
Ahiazu-Mbaise 30.6666667 1.3333333 68.0000000 0.0000000
Ahoada East 0.0000000 15.7894737 84.2105263 0.0000000
Ahoada West 0.0000000 0.0000000 100.0000000 0.0000000
Aiyedade 19.8473282 50.3816794 27.2264631 0.0000000
Aiyedire 16.5714286 50.8571429 32.0000000 0.0000000
Aiyekire (Gbonyin) 20.9302326 58.1395349 20.9302326 4.0697674
Ajaokuta 0.0000000 44.6153846 55.3846154 0.0000000
Ajeromi-Ifelodun 37.5000000 50.0000000 12.5000000 0.0000000
Ajingi 0.0000000 94.0594059 5.9405941 0.0000000
Akamkpa 36.0000000 33.6000000 30.4000000 0.0000000
Akinyele 24.0223464 37.4301676 37.9888268 0.0000000
Akko 0.0000000 76.9480519 23.0519481 0.0000000
Akoko-Edo 0.0000000 19.3548387 80.6451613 0.0000000
Akoko North East 0.0000000 47.5770925 48.0176211 0.0000000
Akoko North West 2.0833333 65.2777778 32.6388889 0.0000000
Akoko South East 0.0000000 48.0769231 49.3589744 0.0000000
Akoko South West 0.0000000 61.7449664 38.2550336 0.0000000
Akpabuyo 30.4687500 23.8281250 45.7031250 0.0000000
Akuku Toru 14.2857143 28.5714286 57.1428571 0.0000000
Akure North 0.0000000 70.2127660 29.7872340 0.0000000
Akure South 0.0000000 40.0000000 56.6666667 0.0000000
Akwanga 0.5555556 62.7777778 36.6666667 0.0000000
Albasu 0.0000000 91.2328767 8.4931507 0.0000000
Aleiro 0.0000000 40.9523810 59.0476190 0.0000000
Alimosho 22.8395062 1.5432099 75.6172840 0.0000000
Alkaleri 0.0000000 90.6250000 9.3750000 0.0000000
Amuwo-Odofin 30.0000000 5.0000000 65.0000000 0.0000000
Anambra East 52.1739130 47.8260870 0.0000000 0.0000000
Anambra West 20.3703704 46.2962963 31.4814815 0.0000000
Anaocha 47.9452055 0.0000000 52.0547945 0.0000000
Andoni 0.0000000 11.7647059 88.2352941 0.0000000
Aninri 60.0000000 40.0000000 0.0000000 0.0000000
Aniocha North 0.0000000 0.0000000 100.0000000 0.0000000
Aniocha South 15.3846154 30.7692308 53.8461538 0.0000000
Anka 0.0000000 84.0000000 16.0000000 0.0000000
Ankpa 3.7037037 27.7777778 66.6666667 0.0000000
Apa 7.6923077 79.4871795 12.8205128 0.0000000
Apapa 100.0000000 0.0000000 0.0000000 0.0000000
Ardo-Kola 16.5876777 72.9857820 10.4265403 0.0000000
Arewa-Dandi 0.0000000 29.0000000 71.0000000 0.0000000
Argungu 0.0000000 63.0769231 36.9230769 0.0000000
Arochukwu 44.0000000 8.0000000 40.0000000 0.0000000
Asa 7.0422535 66.1971831 26.7605634 0.0000000
Asari-Toru 0.0000000 4.2553191 87.2340426 0.0000000
Askira/Uba 0.0000000 66.6666667 30.5555556 0.0000000
Atakumosa East 14.7982063 54.7085202 30.4932735 0.0000000
Atakumosa West 13.0081301 61.3821138 24.7967480 0.0000000
Atiba 32.1428571 21.4285714 46.4285714 0.0000000
Atigbo 22.3214286 47.3214286 30.3571429 0.0000000
Augie 0.0000000 79.1044776 19.4029851 0.0000000
Auyo 0.0000000 99.4604317 0.5395683 0.0000000
Awe 3.2258065 59.1397849 37.6344086 0.0000000
Awgu 26.9230769 50.0000000 19.2307692 0.0000000
Awka North 77.1428571 0.0000000 22.8571429 0.0000000
Awka South 55.0000000 15.0000000 30.0000000 0.0000000
Ayamelum 24.2424242 30.3030303 45.4545455 0.0000000
Babura 0.0000000 85.0111857 14.9888143 0.0000000
Badagry 18.0952381 10.4761905 71.4285714 0.0000000
Bade 0.0000000 67.4050633 32.2784810 0.0000000
Bagudo 0.0000000 79.6116505 19.4174757 0.0000000
Bagwai 0.0000000 93.2142857 6.7857143 0.0000000
Bakassi 0.0000000 0.0000000 0.0000000 0.0000000
Bakori 0.0000000 81.6618911 18.3381089 0.0000000
Bakura 0.0000000 70.5128205 24.3589744 0.0000000
Balanga 0.0000000 84.1509434 15.4716981 0.0000000
Bali 12.0996441 72.2419929 15.6583630 0.0000000
Bama 100.0000000 98.9898990 1.0101010 0.0000000
Barikin Ladi 37.9790941 48.0836237 13.2404181 0.0000000
Baruten 4.2944785 80.9815951 14.7239264 0.6134969
Bassa Kogi 0.0000000 74.6031746 25.3968254 0.0000000
Bassa Plateau 41.2060302 43.2160804 15.5778894 0.0000000
Batagarawa 0.0000000 76.3358779 22.9007634 0.0000000
Batsari 0.0000000 80.6250000 19.3750000 0.0000000
Bauchi 0.0000000 87.0967742 12.9032258 0.0000000
Baure 0.0000000 74.0740741 22.8956229 0.0000000
Bayo 0.0000000 79.1666667 20.8333333 0.0000000
Bebeji 0.0000000 96.3414634 3.6585366 0.0000000
Bekwara 32.4607330 44.5026178 23.0366492 0.0000000
Bende 50.0000000 6.6666667 43.3333333 0.0000000
Biase 33.5877863 36.6412214 29.7709924 0.0000000
Bichi 0.0000000 94.0828402 5.9171598 0.0000000
Bida 0.0000000 5.3941909 94.1908714 0.0000000
Billiri 0.0000000 83.5978836 16.4021164 0.0000000
Bindawa 0.0000000 90.4059041 9.5940959 0.0000000
Binji 0.0000000 57.7319588 42.2680412 0.0000000
Biriniwa 0.0000000 98.4536082 1.1597938 0.0000000
Birni Kudu 0.0000000 100.0000000 0.0000000 0.0000000
Birnin-Gwari 0.0000000 88.0239521 11.9760479 0.0000000
Birnin Kebbi 1.6666667 24.1666667 75.8333333 0.0000000
Birnin Magaji 0.0000000 86.5979381 13.4020619 0.0000000
Biu 0.0000000 72.6027397 27.3972603 0.0000000
Bodinga 0.0000000 26.5306122 73.4693878 0.0000000
Bogoro 0.8064516 90.3225806 8.8709677 0.0000000
Boki 24.0223464 55.8659218 20.1117318 0.0000000
Bokkos 41.6666667 44.2982456 14.0350877 0.0000000
Boluwaduro 11.6279070 55.8139535 32.5581395 0.0000000
Bomadi 0.0000000 75.0000000 25.0000000 0.0000000
Bonny 0.0000000 100.0000000 0.0000000 0.0000000
Borgu 2.7777778 81.4814815 15.7407407 0.0000000
Boripe 7.3446328 67.2316384 25.4237288 0.0000000
Bosso 4.5112782 87.9699248 6.7669173 0.0000000
Brass 0.0000000 13.6363636 86.3636364 0.0000000
Buji 0.0000000 98.4802432 1.5197568 0.0000000
Bukkuyum 0.0000000 94.4444444 5.5555556 0.0000000
Bungudu 0.0000000 74.2857143 25.7142857 0.0000000
Bunkure 0.0000000 90.5511811 9.4488189 0.0000000
Bunza 0.0000000 68.7116564 31.2883436 0.0000000
Bursari 0.0000000 38.1443299 61.8556701 0.0000000
Buruku 21.0227273 64.7727273 13.6363636 0.0000000
Burutu 14.2857143 42.8571429 42.8571429 0.0000000
Bwari 1.6949153 53.3898305 44.9152542 0.0000000
Calabar-Municipal 47.5609756 6.0975610 46.3414634 0.0000000
Calabar South 8.2191781 49.3150685 42.4657534 0.0000000
Chanchaga 0.0000000 95.8333333 4.1666667 0.0000000
Charanchi 0.0000000 86.8020305 12.1827411 0.0000000
Chibok 0.0000000 60.6060606 36.3636364 0.0000000
Chikun 0.0000000 100.0000000 0.0000000 0.0000000
Dala 0.0000000 57.2463768 42.7536232 0.0000000
Damaturu 0.0000000 0.0000000 100.0000000 0.0000000
Damban 0.0000000 85.1351351 14.8648649 0.0000000
Dambatta 0.0000000 82.2660099 17.7339901 0.0000000
Damboa 100.0000000 100.0000000 0.0000000 0.0000000
Dan Musa 0.0000000 76.5625000 23.4375000 0.0000000
Dandi 0.0000000 72.7272727 27.2727273 0.0000000
Dandume 0.0000000 76.5217391 23.4782609 0.0000000
Dange-Shuni 0.0000000 36.3636364 63.6363636 0.0000000
Danja 0.0000000 62.9629630 37.0370370 0.0000000
Darazo 0.0000000 92.2222222 7.7777778 0.0000000
Dass 0.0000000 95.6521739 4.3478261 0.0000000
Daura 0.0000000 63.1578947 36.8421053 0.0000000
Dawakin Kudu 0.0000000 90.5213270 9.4786730 0.0000000
Dawakin Tofa 0.0000000 98.6013986 1.3986014 0.0000000
Degema 12.5000000 12.5000000 75.0000000 0.0000000
Dekina 0.0000000 10.5769231 89.4230769 0.0000000
Demsa 0.0000000 84.6153846 7.6923077 0.0000000
Dikwa 100.0000000 95.0000000 5.0000000 0.0000000
Doguwa 0.0000000 94.5454545 5.4545455 0.0000000
Doma 1.1363636 25.0000000 75.0000000 0.0000000
Donga 12.4324324 75.1351351 12.4324324 0.0000000
Dukku 0.0000000 77.2058824 22.7941176 0.0000000
Dunukofia 11.1111111 4.4444444 84.4444444 0.0000000
Dutse 0.0000000 93.5064935 6.4935065 0.0000000
Dutsi 0.0000000 70.0000000 27.5000000 0.0000000
Dutsin-Ma 0.0000000 88.0258900 11.9741100 0.0000000
Eastern Obolo 0.0000000 24.0000000 76.0000000 0.0000000
Ebonyi 57.6923077 36.5384615 5.7692308 5.0000000
Edati 0.5319149 53.1914894 46.2765957 0.0000000
Ede North 11.5740741 51.8518519 36.1111111 0.0000000
Ede South 23.9726027 64.3835616 10.9589041 0.0000000
Edu 0.0000000 68.7500000 31.2500000 0.0000000
Efon 28.8888889 44.4444444 26.6666667 17.7777778
Egbado North 41.6666667 6.6666667 46.6666667 0.0000000
Egbado South 12.5000000 3.1250000 84.3750000 0.0000000
Egbeda 31.9587629 49.4845361 18.5567010 0.0000000
Egbedore 23.8993711 55.9748428 20.1257862 0.0000000
Egor 0.0000000 0.0000000 100.0000000 0.0000000
Ehime-Mbano 4.2553191 6.3829787 89.3617021 0.0000000
Ejigbo 8.2304527 83.7448560 7.8189300 0.0000000
Ekeremor 0.0000000 18.1818182 81.8181818 0.0000000
Eket 0.0000000 8.8235294 91.1764706 0.0000000
Ekiti 0.0000000 63.3663366 35.1485149 0.0000000
Ekiti East 31.1111111 54.4444444 13.3333333 26.6666667
Ekiti South West 11.1111111 50.6172840 38.2716049 6.7901235
Ekiti West 16.2393162 54.2735043 29.0598291 7.6923077
Ekwusigo 86.1111111 0.0000000 13.8888889 0.0000000
Eleme 0.0000000 50.0000000 50.0000000 0.0000000
Emohua 0.0000000 83.3333333 16.6666667 0.0000000
Emure 27.6923077 41.5384615 30.7692308 13.8461538
Enugu East 21.7391304 34.7826087 43.4782609 0.0000000
Enugu North 12.5000000 37.5000000 16.6666667 0.0000000
Enugu South 52.6315789 31.5789474 42.1052632 0.0000000
Epe 15.4589372 11.1111111 70.0483092 0.4830918
Esan Central 15.6250000 6.2500000 78.1250000 0.0000000
Esan North East 0.0000000 5.8823529 94.1176471 0.0000000
Esan South East 0.0000000 7.1428571 92.8571429 0.0000000
Esan West 0.0000000 20.5882353 79.4117647 0.0000000
Ese-Odo 0.0000000 18.9189189 81.0810811 0.0000000
Esit - Eket 0.0000000 28.9473684 71.0526316 0.0000000
Essien Udim 0.0000000 3.4482759 96.5517241 0.0000000
Etche 31.5789474 5.2631579 63.1578947 0.0000000
Ethiope East 0.0000000 9.0909091 86.3636364 0.0000000
Ethiope West 0.0000000 12.0000000 80.0000000 0.0000000
Eti-Osa 21.0526316 0.0000000 78.9473684 0.0000000
Etim Ekpo 0.0000000 9.8039216 90.1960784 0.0000000
Etinan 0.0000000 20.0000000 80.0000000 0.0000000
Etsako Central 4.1666667 20.8333333 75.0000000 0.0000000
Etsako East 2.3809524 7.1428571 90.4761905 0.0000000
Etsako West 0.0000000 0.0000000 100.0000000 0.0000000
Etung 21.2121212 41.2121212 37.5757576 0.0000000
Ewekoro 55.5555556 1.3888889 43.0555556 0.0000000
Ezeagu 64.7058824 0.0000000 35.2941176 0.0000000
Ezinihitte 39.2857143 0.0000000 60.7142857 0.0000000
Ezza North 41.2087912 56.3186813 2.4725275 2.1978022
Ezza South 37.8980892 57.3248408 4.7770701 3.8216561
Fagge 0.0000000 84.1269841 15.8730159 0.0000000
Fakai 0.0000000 85.4545455 14.5454545 0.0000000
Faskari 0.0000000 85.2173913 14.2028986 0.0000000
Fika 0.0000000 0.0000000 100.0000000 0.0000000
Fufore 0.0000000 46.1538462 53.8461538 0.0000000
Funakaye 0.0000000 73.6842105 26.3157895 0.0000000
Fune 0.0000000 1.4084507 98.5915493 0.0000000
Funtua 0.0000000 62.8571429 37.1428571 0.0000000
Gabasawa 0.0000000 84.3137255 15.6862745 0.0000000
Gada 0.0000000 66.1016949 33.8983051 0.0000000
Gagarawa 0.0000000 99.1011236 0.8988764 0.0000000
Gamawa 0.0000000 95.0338600 4.9661400 0.0000000
Ganjuwa 0.0000000 95.0617284 4.9382716 0.0000000
Ganye 0.0000000 71.4285714 28.5714286 0.0000000
Garki 0.0000000 96.9976905 2.7713626 0.0000000
Garko 0.0000000 84.2696629 14.6067416 0.0000000
Garum Mallam 0.0000000 92.6380368 7.3619632 0.0000000
Gashaka 13.3333333 77.5757576 9.0909091 0.0000000
Gassol 21.5094340 63.7735849 14.3396226 0.0000000
Gaya 0.0000000 100.0000000 0.0000000 0.0000000
Gbako 0.0000000 53.7142857 45.1428571 0.0000000
Gboko 24.2268041 53.0927835 26.8041237 0.0000000
Geidam 0.0000000 0.0000000 0.0000000 0.0000000
Gezawa 0.0000000 89.3992933 10.6007067 0.0000000
Giade 0.0000000 96.3636364 3.6363636 0.0000000
Girei 0.0000000 60.0000000 40.0000000 0.0000000
Giwa 0.0000000 94.5736434 5.4263566 0.0000000
Gokana 0.0000000 0.0000000 100.0000000 0.0000000
Gombe 0.0000000 48.5714286 51.4285714 0.0000000
Gombi 0.0000000 77.7777778 22.2222222 0.0000000
Goronyo 0.0000000 57.1428571 42.8571429 0.0000000
Gubio 0.0000000 0.0000000 0.0000000 0.0000000
Gudu 0.0000000 47.5000000 52.5000000 0.0000000
Gujba 0.0000000 0.0000000 0.0000000 0.0000000
Gulani 0.0000000 0.0000000 100.0000000 0.0000000
Guma 20.5128205 64.9572650 14.5299145 0.0000000
Gumel 0.0000000 62.4020888 37.5979112 0.0000000
Gummi 0.0000000 92.5373134 7.4626866 0.0000000
Gurara 25.6097561 62.1951220 12.1951220 0.0000000
Guri 0.0000000 98.8338192 1.1661808 0.0000000
Gusau 0.0000000 87.5000000 12.5000000 0.0000000
Guyuk 0.0000000 59.0909091 40.9090909 0.0000000
Guzamala 0.0000000 0.0000000 0.0000000 0.0000000
Gwadabawa 0.0000000 69.2982456 30.7017544 0.0000000
Gwagwalada 1.6949153 61.0169492 38.1355932 0.0000000
Gwale 0.0000000 85.4838710 14.5161290 0.0000000
Gwandu 0.0000000 80.6122449 19.3877551 0.0000000
Gwaram 0.0000000 89.4230769 8.6538462 0.0000000
Gwarzo 0.0000000 86.8421053 11.8421053 0.0000000
Gwer East 3.9682540 79.3650794 15.8730159 0.0000000
Gwer West 4.7619048 76.1904762 19.0476190 0.0000000
Gwiwa 0.0000000 98.0198020 1.9801980 0.0000000
Gwoza 100.0000000 65.9090909 34.0909091 0.0000000
Hadejia 0.0000000 93.7500000 6.2500000 0.0000000
Hawul 0.0000000 65.6250000 34.3750000 0.0000000
Hong 0.0000000 75.0000000 25.0000000 0.0000000
Ibadan North 33.3333333 6.0606061 60.6060606 0.0000000
Ibadan North East 7.0866142 30.7086614 62.2047244 0.0000000
Ibadan North West 4.5977011 5.7471264 88.5057471 0.0000000
Ibadan South East 22.2222222 19.4444444 55.5555556 2.7777778
Ibadan South West 8.4112150 23.3644860 68.2242991 0.0000000
Ibaji 0.0000000 38.2352941 61.7647059 0.0000000
Ibarapa Central 20.5741627 57.8947368 21.5311005 0.0000000
Ibarapa East 28.4313725 43.1372549 28.4313725 0.0000000
Ibarapa North 21.7171717 53.5353535 24.7474747 0.0000000
Ibeju/Lekki 17.2932331 3.7593985 78.9473684 0.0000000
Ibeno 0.0000000 25.0000000 75.0000000 0.0000000
Ibesikpo Asutan 0.0000000 1.8518519 98.1481481 0.0000000
Ibi 13.4146341 78.0487805 8.5365854 0.0000000
Ibiono Ibom 0.0000000 7.8431373 92.1568627 0.0000000
Idah 0.0000000 49.1525424 50.8474576 0.0000000
Idanre 0.9615385 86.5384615 12.5000000 0.0000000
Ideato North 64.2857143 0.0000000 35.7142857 0.0000000
Ideato South 43.4782609 0.0000000 56.5217391 0.0000000
Idemili North 85.7142857 2.0408163 12.2448980 0.0000000
Idemili South 80.3571429 3.5714286 16.0714286 0.0000000
Ido 19.4174757 32.0388350 48.5436893 0.0000000
Ido-Osi 54.1666667 22.9166667 18.7500000 31.2500000
Ifako-Ijaye 16.3934426 1.6393443 81.9672131 0.0000000
Ife Central 2.5974026 52.5974026 44.8051948 0.0000000
Ife East 8.1218274 46.1928934 45.6852792 0.0000000
Ife North 7.2992701 52.5547445 39.4160584 0.0000000
Ife South 10.1123596 64.0449438 25.2808989 0.0000000
Ifedayo 22.6190476 48.8095238 28.5714286 0.0000000
Ifedore 0.0000000 74.1935484 25.8064516 0.0000000
Ifelodun Kwara 0.4991681 69.3843594 30.1164725 0.0000000
Ifelodun Osun 6.3492063 57.9365079 35.7142857 0.0000000
Ifo 78.3132530 0.0000000 21.6867470 0.0000000
Igabi 0.0000000 93.1740614 6.8259386 0.0000000
Igalamela-Odolu 0.0000000 25.0000000 75.0000000 0.0000000
Igbo-Etiti 45.4545455 6.8181818 45.4545455 2.2727273
Igbo-Eze North 45.2380952 0.0000000 54.7619048 0.0000000
Igbo-Eze South 52.9411765 0.0000000 47.0588235 0.0000000
Igueben 0.0000000 0.0000000 100.0000000 0.0000000
Ihiala 56.4885496 43.5114504 0.0000000 0.0000000
Ihitte/Uboma 27.7777778 5.5555556 58.3333333 8.3333333
Ijebu East 77.4193548 3.2258065 19.3548387 0.0000000
Ijebu North 25.0000000 20.3125000 48.4375000 6.2500000
Ijebu North East 24.4444444 4.4444444 71.1111111 0.0000000
Ijebu Ode 20.6896552 5.1724138 74.1379310 0.0000000
Ijero 20.8737864 54.3689320 24.2718447 16.9902913
Ijumu 0.0000000 49.1803279 50.0000000 0.0000000
Ika 0.0000000 14.2857143 85.7142857 0.0000000
Ika North East 0.0000000 0.0000000 100.0000000 0.0000000
Ika South 0.0000000 0.0000000 100.0000000 0.0000000
Ikara 0.0000000 86.8613139 13.1386861 0.0000000
Ikeduru 53.3333333 0.0000000 46.6666667 0.0000000
Ikeja 33.3333333 14.2857143 42.8571429 0.0000000
Ikenne 52.6315789 0.0000000 47.3684211 0.0000000
Ikere 10.3092784 62.8865979 26.8041237 4.1237113
Ikole 29.7297297 52.9729730 17.2972973 22.7027027
Ikom 26.3681592 40.2985075 33.3333333 0.0000000
Ikono 0.0000000 6.3492063 93.6507937 0.0000000
Ikorodu 19.1666667 2.0833333 70.0000000 8.7500000
Ikot Abasi 0.0000000 15.5172414 84.4827586 0.0000000
Ikot Ekpene 0.0000000 2.1276596 97.8723404 0.0000000
Ikpoba-Okha 0.0000000 1.5151515 98.4848485 0.0000000
Ikwerre 0.0000000 45.4545455 54.5454545 0.0000000
Ikwo 63.5220126 33.3333333 3.1446541 7.5471698
Ikwuano 47.6635514 4.6728972 47.6635514 0.0000000
Ila 17.9190751 47.3988439 34.1040462 0.5780347
Ilaje 0.0000000 0.0000000 100.0000000 0.0000000
Ile-Oluji-Okeigbo 0.7194245 56.8345324 42.4460432 0.0000000
Ilejemeji 23.8095238 47.6190476 26.1904762 14.2857143
Ilesha East 16.5467626 30.9352518 52.5179856 0.0000000
Ilesha West 4.1237113 53.6082474 42.2680412 0.0000000
Illela 0.0000000 47.7272727 52.2727273 0.0000000
Ilorin East 0.0000000 64.8648649 35.1351351 0.0000000
Ilorin South 0.0000000 66.6666667 33.3333333 0.0000000
Ilorin West 0.0000000 52.9411765 46.4052288 0.0000000
Imeko-Afon 87.8787879 0.0000000 12.1212121 0.0000000
Ingawa 0.0000000 92.4242424 7.0707071 0.0000000
Ini 0.0000000 21.7391304 78.2608696 0.0000000
Ipokia 25.3968254 3.1746032 71.4285714 12.6984127
Irele 0.0000000 14.9425287 85.0574713 0.0000000
Irepo 25.4237288 36.4406780 38.1355932 0.0000000
Irepodun Kwara 0.0000000 61.5384615 37.6923077 0.0000000
Irepodun Osun 12.5000000 31.7307692 54.8076923 0.0000000
Irepodun/Ifelodun 37.1681416 37.1681416 25.6637168 31.8584071
Irewole 9.9099099 45.9459459 44.1441441 0.0000000
Isa 0.0000000 58.4905660 39.6226415 0.0000000
Ise/Orun 31.4285714 45.7142857 22.8571429 8.5714286
Iseyin 63.2075472 9.4339623 27.3584906 0.0000000
Ishielu 35.6557377 58.1967213 6.1475410 5.7377049
Isi-Uzo 24.1379310 75.8620690 12.0689655 0.0000000
Isiala-Ngwa North 41.5730337 16.8539326 41.5730337 0.0000000
Isiala-Ngwa South 23.4375000 3.1250000 73.4375000 0.0000000
Isiala Mbano 13.1578947 0.0000000 86.8421053 0.0000000
Isin 2.5806452 57.4193548 40.0000000 0.6451613
Isiukwuato 32.7868852 8.1967213 59.0163934 0.0000000
Isokan 19.5121951 54.4715447 26.0162602 0.0000000
Isoko North 0.0000000 7.6923077 92.3076923 0.0000000
Isoko South 0.0000000 12.5000000 87.5000000 0.0000000
Isu 62.5000000 0.0000000 37.5000000 0.0000000
Itas/Gadau 0.0000000 87.4125874 12.5874126 0.0000000
Itesiwaju 33.8028169 19.7183099 46.4788732 0.0000000
Itu 1.6666667 5.0000000 93.3333333 0.0000000
Ivo 45.4545455 38.6363636 15.9090909 4.5454545
Iwajowa 21.1764706 50.1960784 28.6274510 0.0000000
Iwo 20.6250000 34.3750000 45.0000000 0.0000000
Izzi 68.6520376 28.2131661 3.1347962 1.8808777
Jaba 0.0000000 96.7320261 3.2679739 0.0000000
Jada 0.0000000 57.1428571 42.8571429 0.0000000
Jahun 0.0000000 95.8333333 4.1666667 0.0000000
Jakusko 0.0000000 17.1428571 80.0000000 0.0000000
Jalingo 10.8695652 76.8115942 12.3188406 0.0000000
Jama'are 0.0000000 91.2698413 8.7301587 0.0000000
Jega 0.0000000 39.8648649 60.1351351 0.0000000
Jema'a 0.0000000 91.8088737 8.1911263 0.0000000
Jere 25.6578947 43.4210526 55.9210526 0.0000000
Jibia 0.0000000 83.1578947 16.8421053 0.0000000
Jos East 41.7910448 46.7661692 10.9452736 0.0000000
Jos North 27.6923077 36.9230769 35.3846154 0.0000000
Jos South 31.0160428 54.0106952 10.1604278 0.0000000
Kabba/Bunu 0.0000000 63.0136986 36.9863014 0.0000000
Kabo 0.0000000 100.0000000 0.0000000 0.0000000
Kachia 0.0000000 95.6730769 3.8461538 0.0000000
Kaduna North 0.0000000 77.5862069 22.4137931 0.0000000
Kaduna South 0.0000000 93.2432432 6.7567568 0.0000000
Kafin Hausa 0.0000000 100.0000000 0.0000000 0.0000000
Kafur 0.0000000 83.7398374 16.2601626 0.0000000
Kaga 0.0000000 0.0000000 0.0000000 0.0000000
Kagarko 0.0000000 96.2162162 3.7837838 0.0000000
Kaiama 4.8543689 85.4368932 9.7087379 0.0000000
Kaita 0.0000000 80.0664452 19.9335548 0.0000000
Kajola 21.9101124 49.4382022 28.6516854 0.0000000
Kajuru 0.0000000 95.1048951 4.8951049 0.0000000
Kala/Balge 0.0000000 0.0000000 0.0000000 0.0000000
Kalgo 0.0000000 60.0000000 40.0000000 0.0000000
Kaltungo 0.0000000 82.0000000 18.0000000 0.0000000
Kanam 42.0689655 50.0000000 6.8965517 0.0000000
Kankara 0.0000000 68.5039370 31.4960630 0.0000000
Kanke 31.6964286 61.6071429 6.6964286 0.0000000
Kankia 0.0000000 82.0143885 17.9856115 0.0000000
Kano Municipal 0.0000000 69.3965517 30.6034483 0.0000000
Karasuwa 0.0000000 66.1764706 33.8235294 0.0000000
Karaye 0.0000000 100.0000000 0.0000000 0.0000000
Karim-Lamido 18.5185185 62.3456790 19.1358025 0.0000000
Karu 7.8651685 33.7078652 58.4269663 0.0000000
Katagum 0.0000000 72.8813559 27.1186441 0.0000000
Katcha 17.3913043 71.7391304 10.8695652 0.0000000
Katsina 0.0000000 57.9545455 42.0454545 0.0000000
Katsina-Ala 33.5443038 55.6962025 10.7594937 0.0000000
Kaugama 0.0000000 99.4555354 0.5444646 0.0000000
Kaura 0.0000000 91.4893617 8.5106383 0.0000000
Kaura Namoda 0.0000000 89.4736842 10.5263158 0.0000000
Kauru 0.0000000 98.2658960 1.7341040 0.0000000
Kazaure 0.0000000 56.3968668 43.6031332 0.0000000
Keana 0.0000000 63.6363636 36.3636364 0.0000000
Kebbe 0.0000000 64.0625000 34.3750000 0.0000000
Keffi 0.0000000 22.0779221 77.9220779 0.0000000
Khana 6.6666667 0.0000000 93.3333333 0.0000000
Kibiya 0.0000000 93.4272300 3.7558685 0.4694836
Kirfi 10.3448276 80.4597701 18.3908046 1.1494253
Kiri Kasamma 0.0000000 55.5110220 44.2885772 0.0000000
Kiru 1.3215859 91.1894273 8.8105727 0.0000000
Kiyawa 0.0000000 91.8478261 1.6304348 0.0000000
Kogi 0.0000000 52.8571429 45.7142857 0.0000000
Koko/Besse 0.0000000 80.4054054 19.5945946 0.0000000
Kokona 0.9523810 67.6190476 31.4285714 0.0000000
Kolokuma/Opokuma 5.0000000 0.0000000 95.0000000 0.0000000
Konduga 33.3333333 66.6666667 33.3333333 0.0000000
Konshisha 29.3628809 65.9279778 4.9861496 0.0000000
Kontagora 2.4691358 92.5925926 4.9382716 0.0000000
Kosofe 13.7931034 29.3103448 56.8965517 0.0000000
Kubau 0.0000000 95.8333333 4.1666667 0.0000000
Kudan 0.0000000 100.0000000 0.0000000 0.0000000
Kuje 8.3916084 28.6713287 62.9370629 0.0000000
Kukawa 0.0000000 0.0000000 0.0000000 0.0000000
Kumbotso 0.0000000 92.1568627 7.8431373 0.0000000
Kunchi 0.0000000 97.6562500 2.3437500 0.0000000
Kura 0.0000000 89.3129771 10.6870229 0.0000000
Kurfi 0.0000000 86.4077670 13.5922330 0.0000000
Kurmi 15.3061224 76.5306122 7.1428571 0.0000000
Kusada 0.0000000 88.2352941 11.7647059 0.0000000
Kwali 1.1235955 55.0561798 43.8202247 0.0000000
Kwami 0.0000000 77.3333333 22.6666667 0.0000000
Kwande 12.6436782 73.5632184 13.7931034 0.0000000
Kware 0.0000000 42.7480916 51.9083969 0.0000000
Kwaya Kusar 0.0000000 100.0000000 0.0000000 0.0000000
Lafia 4.0590406 30.9963100 64.9446494 0.3690037
Lagelu 16.8000000 44.0000000 39.2000000 0.0000000
Lagos Island 0.0000000 13.5135135 86.4864865 0.0000000
Lagos Mainland 26.1904762 2.3809524 69.0476190 0.0000000
Lamurde 0.0000000 100.0000000 0.0000000 0.0000000
Langtang North 34.1333333 59.7333333 6.1333333 0.0000000
Langtang South 35.8695652 44.5652174 18.4782609 0.0000000
Lapai 0.8474576 61.8644068 37.2881356 0.0000000
Lau 14.2045455 75.0000000 10.7954545 0.0000000
Lavun 1.0000000 44.0000000 55.0000000 0.0000000
Lere 0.0000000 98.3695652 1.6304348 0.0000000
Logo 31.2925170 56.4625850 12.9251701 0.0000000
Lokoja 0.0000000 52.6315789 47.3684211 0.0000000
Machina 0.0000000 72.9729730 27.0270270 0.0000000
Madagali 0.0000000 0.0000000 0.0000000 0.0000000
Madobi 0.0000000 96.0000000 1.1428571 0.0000000
Mafa 100.0000000 0.0000000 100.0000000 0.0000000
Magama 5.0000000 92.0000000 3.0000000 0.0000000
Magumeri 0.0000000 77.7777778 22.2222222 0.0000000
Mai'adua 0.0000000 62.5498008 37.4501992 0.0000000
Maiduguri 31.5789474 16.5413534 82.7067669 0.0000000
Maigatari 0.0000000 93.5294118 6.4705882 0.0000000
Maiha 0.0000000 82.3529412 17.6470588 0.0000000
Maiyama 0.0000000 66.0000000 34.0000000 0.0000000
Makoda 0.0000000 19.8113208 80.1886792 0.0000000
Makurdi 3.2608696 63.0434783 33.6956522 0.0000000
Malam Madori 0.0000000 98.4126984 1.5873016 0.0000000
Malumfashi 0.0000000 85.4166667 12.5000000 0.0000000
Mangu 33.8607595 53.7974684 12.0253165 0.0000000
Mani 0.0000000 81.3793103 15.1724138 0.0000000
Maradun 0.0000000 78.1250000 20.8333333 0.0000000
Mariga 0.8474576 87.2881356 11.8644068 0.0000000
Markafi 0.0000000 91.2280702 8.7719298 0.0000000
Marte 0.0000000 0.0000000 0.0000000 0.0000000
Maru 0.0000000 74.2514970 24.5508982 0.0000000
Mashegu 3.5714286 73.2142857 23.2142857 0.0000000
Mashi 0.0000000 62.2950820 36.8852459 0.0000000
Matazu 0.0000000 83.4196891 14.5077720 0.0000000
Mayo-Belwa 0.0000000 91.6666667 8.3333333 0.0000000
Mbaitoli 52.7272727 0.0000000 47.2727273 0.0000000
Mbo 0.0000000 16.6666667 83.3333333 0.0000000
Michika 0.0000000 80.0000000 20.0000000 0.0000000
Miga 0.0000000 95.3125000 4.6875000 0.0000000
Mikang 34.5762712 55.9322034 8.8135593 0.0000000
Minjibir 0.0000000 90.2912621 9.7087379 0.0000000
Misau 0.0000000 93.8931298 6.1068702 0.0000000
Mkpat Enin 0.0000000 28.6956522 71.3043478 0.0000000
Moba 38.1578947 40.7894737 21.0526316 32.8947368
Mobbar 0.0000000 0.0000000 0.0000000 0.0000000
Mokwa 0.0000000 42.1052632 57.8947368 0.0000000
Monguno 100.0000000 96.1538462 3.8461538 0.0000000
Mopa-Muro 0.0000000 66.2790698 33.7209302 0.0000000
Moro 3.4285714 82.8571429 13.7142857 0.0000000
Mubi North 0.0000000 0.0000000 100.0000000 0.0000000
Mubi South 0.0000000 0.0000000 100.0000000 0.0000000
Musawa 0.0000000 90.9090909 8.3916084 0.0000000
Mushin 9.3750000 0.0000000 90.6250000 0.0000000
Muya 0.0000000 93.6842105 6.3157895 0.0000000
Nafada 0.0000000 62.8378378 37.1621622 0.0000000
Nangere 0.0000000 5.4054054 91.8918919 0.0000000
Nasarawa Kano 0.0000000 48.0000000 52.0000000 0.0000000
Nasarawa 6.2068966 51.7241379 41.3793103 0.0000000
Nasarawa-Eggon 1.8867925 50.3144654 47.1698113 0.0000000
Ndokwa East 0.0000000 32.5301205 53.0120482 14.4578313
Ndokwa West 0.0000000 15.2173913 84.7826087 0.0000000
Nembe 0.0000000 18.6046512 74.4186047 2.3255814
Ngala 100.0000000 93.0000000 7.0000000 0.0000000
Nganzai 0.0000000 0.0000000 0.0000000 0.0000000
Ngaski 0.0000000 93.9130435 6.0869565 0.0000000
Ngor-Okpala 48.5294118 2.9411765 48.5294118 0.0000000
Nguru 0.0000000 88.0630631 11.9369369 0.0000000
Ningi 0.0000000 96.7391304 3.2608696 0.0000000
Njaba 46.6666667 0.0000000 53.3333333 0.0000000
Njikoka 29.4117647 41.1764706 29.4117647 0.0000000
Nkanu East 61.1111111 37.0370370 1.8518519 1.8518519
Nkanu West 32.9113924 44.3037975 21.5189873 1.2658228
Nkwerre 3.0303030 9.0909091 87.8787879 0.0000000
Nnewi North 43.5897436 2.5641026 53.8461538 0.0000000
Nnewi South 90.9090909 3.0303030 6.0606061 0.0000000
Nsit Atai 0.0000000 13.2352941 86.7647059 0.0000000
Nsit Ibom 0.0000000 8.1632653 91.8367347 0.0000000
Nsit Ubium 0.0000000 9.0909091 90.9090909 0.0000000
Nsukka 62.5000000 0.0000000 39.2857143 0.0000000
Numan 0.0000000 0.0000000 100.0000000 0.0000000
Nwangele 25.0000000 3.5714286 71.4285714 0.0000000
Obafemi-Owode 40.9090909 7.5757576 51.5151515 0.0000000
Obanliku 28.5714286 41.7582418 29.6703297 0.0000000
Nasarawa Nasarawa 21.6867470 69.8795181 7.8313253 0.0000000
Obi Nasarawa 0.0000000 53.4090909 46.5909091 0.0000000
Obi Ngwa 16.7630058 27.1676301 56.0693642 0.0000000
Obia/Akpor 0.0000000 25.6000000 72.8000000 0.0000000
Obokun 4.4554455 50.9900990 44.5544554 0.0000000
Obot Akara 3.1746032 7.9365079 88.8888889 0.0000000
Obowo 40.9090909 0.0000000 59.0909091 0.0000000
Obubra 16.3090129 57.5107296 26.1802575 1.2875536
Obudu 24.6861925 42.2594142 33.0543933 0.0000000
Odeda 51.3274336 16.8141593 30.9734513 0.0000000
Odigbo 0.0000000 50.0000000 50.0000000 0.0000000
Odo-Otin 15.4605263 50.9868421 33.5526316 0.0000000
Odogbolu 19.4805195 6.4935065 72.7272727 0.0000000
Odukpani 5.3892216 28.1437126 65.2694611 0.0000000
Offa 0.0000000 60.1063830 39.3617021 0.0000000
Ofu 0.0000000 20.5882353 79.4117647 0.0000000
Ogba/Egbema/Ndoni 24.1935484 6.4516129 69.3548387 0.0000000
Ogbadibo 19.7530864 20.9876543 60.4938272 0.0000000
Ogbaru 30.4347826 23.9130435 45.6521739 0.0000000
Ogbia 0.0000000 87.5000000 12.5000000 0.0000000
Ogbomosho North 3.0000000 9.0000000 88.0000000 0.0000000
Ogbomosho South 0.8620690 34.4827586 64.6551724 0.0000000
Ogo Oluwa 4.3010753 41.9354839 53.7634409 0.0000000
Ogoja 37.7289377 42.1245421 20.1465201 0.0000000
Ogori/Magongo 0.0000000 27.0833333 70.8333333 2.0833333
Ogu/Bolo 0.0000000 0.0000000 100.0000000 0.0000000
Ogun waterside 4.0540541 9.4594595 86.4864865 0.0000000
Oguta 23.2323232 27.2727273 49.4949495 0.0000000
Ohafia 28.9473684 7.8947368 63.1578947 0.0000000
Ohaji/Egbema 12.9213483 68.5393258 17.9775281 0.0000000
Ohaozara 35.6363636 53.0909091 11.2727273 2.9090909
Ohaukwu 39.7759104 56.8627451 3.6414566 5.0420168
Ohimini 0.0000000 90.7692308 9.2307692 0.0000000
Oji-River 52.6315789 2.6315789 44.7368421 0.0000000
Ojo 45.9770115 3.4482759 50.5747126 0.0000000
Oju 1.4652015 93.0402930 5.4945055 0.0000000
Oke-Ero 0.4830918 71.4975845 25.6038647 0.4830918
Okehi 0.0000000 59.4594595 39.1891892 0.0000000
Okene 0.0000000 19.0000000 80.0000000 0.0000000
Okigwe 9.7560976 0.0000000 87.8048780 0.0000000
Okitipupa 0.0000000 16.0000000 84.0000000 0.0000000
Okobo 1.3513514 8.1081081 90.5405405 0.0000000
Okpe 23.0769231 7.6923077 61.5384615 0.0000000
Okpokwu 13.5593220 86.4406780 10.1694915 0.0000000
Okrika 0.0000000 33.3333333 50.0000000 16.6666667
Ola-oluwa 39.1891892 41.8918919 18.9189189 0.0000000
Olamabolo 0.0000000 0.0000000 100.0000000 0.0000000
Olorunda 12.1546961 55.2486188 32.5966851 0.0000000
Olorunsogo 36.3636364 25.2525253 37.3737374 0.0000000
Oluyole 36.4864865 45.9459459 13.5135135 0.0000000
Omala 0.0000000 48.6486486 51.3513514 0.0000000
Omumma 0.0000000 40.0000000 60.0000000 0.0000000
Ona-Ara 22.8070175 42.9824561 34.2105263 0.0000000
Ondo East 0.0000000 58.5185185 41.4814815 0.0000000
Ondo West 0.0000000 51.5527950 48.4472050 0.0000000
Onicha 31.9634703 63.4703196 4.1095890 5.9360731
Onitsha North 84.6153846 0.0000000 15.3846154 0.0000000
Onitsha South 41.6666667 8.3333333 50.0000000 0.0000000
Onna 0.0000000 19.0476190 80.9523810 0.0000000
Opobo/Nkoro 0.0000000 45.4545455 45.4545455 0.0000000
Oredo 0.0000000 4.3478261 95.6521739 0.0000000
Orelope 35.7142857 46.9387755 17.3469388 0.0000000
Orhionmwon 0.0000000 0.9009009 99.0990991 0.0000000
Ori Ire 41.8410042 43.0962343 15.0627615 0.0000000
Oriade 18.1467181 43.2432432 38.6100386 0.7722008
Orlu 35.2941176 5.8823529 58.8235294 0.0000000
Orolu 7.5000000 50.8333333 40.0000000 0.0000000
Oron 0.0000000 20.0000000 80.0000000 0.0000000
Orsu 20.0000000 5.0000000 75.0000000 0.0000000
Oru East 61.5384615 0.0000000 38.4615385 0.0000000
Oru West 58.5365854 7.3170732 34.1463415 0.0000000
Oruk Anam 0.0000000 22.0338983 77.9661017 0.0000000
Orumba North 57.5757576 10.6060606 31.8181818 0.0000000
Orumba South 48.4848485 0.0000000 51.5151515 0.0000000
Ose 0.0000000 46.0000000 52.0000000 0.0000000
Oshimili North 0.0000000 3.0303030 96.9696970 0.0000000
Oshimili South 0.0000000 8.6956522 91.3043478 0.0000000
Oshodi-Isolo 31.9148936 4.2553191 61.7021277 0.0000000
Osisioma Ngwa 43.1818182 6.8181818 70.4545455 0.0000000
Osogbo 8.7209302 36.0465116 54.0697674 0.0000000
Oturkpo 11.2903226 75.8064516 12.9032258 0.0000000
Ovia North East 8.4337349 0.0000000 91.5662651 0.0000000
Ovia South West 3.8961039 1.2987013 94.8051948 0.0000000
Owan East 1.5873016 6.3492063 92.0634921 0.0000000
Owan West 0.0000000 6.8181818 93.1818182 0.0000000
Owerri-Municipal 29.0322581 12.9032258 58.0645161 0.0000000
Owerri North 25.0000000 2.9411765 72.0588235 0.0000000
Owerri West 19.6721311 31.1475410 49.1803279 0.0000000
Owo 0.0000000 46.4088398 53.5911602 0.0000000
Oye 15.4929577 55.6338028 28.8732394 9.1549296
Oyi 38.3561644 0.0000000 61.6438356 0.0000000
Oyigbo 0.0000000 41.1764706 58.8235294 0.0000000
Oyo East 26.1363636 30.6818182 43.1818182 0.0000000
Oyo West 53.1250000 10.9375000 35.9375000 0.0000000
Oyun 0.0000000 70.7462687 28.3582090 0.0000000
Paikoro 1.0638298 88.2978723 10.6382979 0.0000000
Pankshin 42.9648241 48.7437186 8.2914573 0.0000000
Patani 0.0000000 18.1818182 72.7272727 0.0000000
Pategi 2.9090909 67.6363636 29.4545455 0.0000000
Port-Harcourt 0.0000000 69.5652174 5.2173913 23.4782609
Potiskum 0.0000000 25.0000000 75.0000000 0.0000000
Qua'an Pan 36.8115942 54.4927536 8.1159420 0.0000000
Rabah 0.0000000 58.7301587 41.2698413 0.0000000
Rafi 0.0000000 87.1212121 12.8787879 0.0000000
Rano 0.0000000 91.0000000 8.0000000 0.0000000
Remo North 29.5454545 13.6363636 56.8181818 0.0000000
Rijau 7.2847682 78.8079470 13.9072848 0.0000000
Rimi 0.0000000 71.4285714 28.5714286 0.0000000
Rimin Gado 0.0000000 96.9879518 1.8072289 0.0000000
Ringim 0.0000000 87.5000000 11.8055556 0.0000000
Riyom 41.4473684 47.0394737 10.8552632 0.0000000
Rogo 0.0000000 88.6486486 11.3513514 0.0000000
Roni 0.0000000 94.6666667 5.3333333 0.0000000
Sabon-Gari 0.0000000 76.2626263 23.7373737 0.0000000
Sabon Birni 0.0000000 80.5194805 19.4805195 0.0000000
Sabuwa 0.0000000 74.4186047 23.2558140 0.0000000
Safana 0.0000000 93.7888199 4.9689441 0.0000000
Sagbama 0.0000000 20.9677419 74.1935484 4.8387097
Sakaba 0.0000000 89.6551724 9.6551724 0.0000000
Saki East 13.7614679 44.9541284 41.2844037 0.0000000
Saki West 22.9729730 48.6486486 27.7027027 0.0000000
Sandamu 0.0000000 70.6422018 23.8532110 0.0000000
Sanga 0.0000000 94.1558442 5.8441558 0.0000000
Sapele 0.0000000 8.3333333 91.6666667 0.0000000
Sardauna 16.0000000 74.0000000 10.0000000 0.0000000
Shagamu 35.2459016 1.6393443 63.1147541 0.0000000
Shagari 0.0000000 17.3076923 82.6923077 0.0000000
Shanga 0.0000000 85.4368932 14.5631068 0.0000000
Shani 0.0000000 63.4146341 36.5853659 0.0000000
Shanono 0.0000000 77.1084337 22.8915663 0.0000000
Shelleng 0.0000000 88.8888889 11.1111111 0.0000000
Shendam 48.1308411 45.0934579 6.7757009 0.0000000
Shinkafi 0.0000000 88.5350318 11.4649682 0.0000000
Shira 0.0000000 89.7560976 10.2439024 0.0000000
Shiroro 11.0091743 79.8165138 9.1743119 0.0000000
Shomgom 0.0000000 88.8446215 11.1553785 0.0000000
Shomolu 21.4285714 0.0000000 78.5714286 0.0000000
Silame 0.0000000 46.6666667 53.3333333 0.0000000
Soba 0.0000000 91.7675545 8.2324455 0.0000000
Sokoto North 0.0000000 15.1515152 84.8484848 0.0000000
Sokoto South 0.0000000 32.0000000 68.0000000 0.0000000
Song 0.0000000 78.5714286 21.4285714 0.0000000
Southern Ijaw 0.0000000 9.6774194 35.4838710 0.0000000
Sule-Tankarkar 0.0000000 89.8969072 10.1030928 0.0000000
Suleja 0.0000000 80.4878049 19.5121951 0.0000000
Sumaila 0.0000000 96.3898917 3.2490975 0.0000000
Suru 0.0000000 80.0000000 20.0000000 0.0000000
Obi Benue 9.8360656 19.6721311 67.2131148 1.6393443
Surulere Lagos 3.1746032 52.3809524 41.7989418 0.0000000
Tafa 0.0000000 68.7500000 31.2500000 0.0000000
Tafawa-Balewa 1.5625000 87.5000000 12.5000000 0.0000000
Tai 0.0000000 0.0000000 100.0000000 0.0000000
Takai 0.0000000 93.0337079 6.9662921 0.0000000
Takum 7.7777778 76.6666667 15.0000000 0.0000000
Talata Mafara 0.0000000 72.1925134 27.8074866 0.0000000
Tambuwal 0.0000000 48.3146067 51.6853933 0.0000000
Tangaza 0.0000000 56.2500000 43.7500000 0.0000000
Tarauni 0.0000000 88.0597015 10.4477612 1.4925373
Tarka 19.6428571 74.1071429 6.2500000 0.0000000
Tarmua 0.0000000 5.5555556 94.4444444 0.0000000
Taura 0.0000000 98.7969925 0.9022556 0.0000000
Tofa 0.0000000 89.1891892 10.8108108 0.0000000
Toro 3.0075188 93.9849624 5.2631579 0.0000000
Toto 1.7094017 35.8974359 62.3931624 0.0000000
Toungo 0.0000000 80.0000000 20.0000000 0.0000000
Tsafe 0.0000000 84.4221106 15.0753769 0.0000000
Tsanyawa 0.0000000 96.1290323 3.8709677 0.0000000
Tudun Wada 0.0000000 84.6153846 14.2011834 0.0000000
Tureta 0.0000000 58.5585586 41.4414414 0.0000000
Udenu 72.7272727 0.0000000 27.2727273 0.0000000
Udi 67.6190476 0.9523810 28.5714286 0.0000000
Udu 0.0000000 72.7272727 27.2727273 0.0000000
Udung Uko 0.0000000 11.1111111 88.8888889 0.0000000
Ughelli North 7.5471698 24.5283019 60.3773585 1.8867925
Ughelli South 0.0000000 32.0000000 68.0000000 0.0000000
Ugwunagbo 23.8636364 47.7272727 25.0000000 0.0000000
Uhunmwonde 3.1746032 3.1746032 92.0634921 0.0000000
Ukanafun 0.0000000 38.0952381 61.9047619 0.0000000
Ukum 26.5822785 63.2911392 10.1265823 0.0000000
Ukwa East 7.7777778 53.3333333 38.8888889 0.0000000
Ukwa West 18.5483871 26.6129032 54.8387097 0.0000000
Ukwuani 0.0000000 20.3703704 79.6296296 0.0000000
Umu-Nneochi 56.8181818 9.0909091 34.0909091 0.0000000
Umuahia North 35.1351351 16.2162162 47.2972973 0.0000000
Umuahia South 43.6893204 7.7669903 48.5436893 0.0000000
Ungogo 0.0000000 95.3642384 4.6357616 0.0000000
Unuimo 50.0000000 33.3333333 16.6666667 0.0000000
Uruan 0.0000000 10.4166667 85.4166667 0.0000000
Urue-Offong/Oruko 0.0000000 17.8571429 82.1428571 0.0000000
Ushongo 29.6943231 63.7554585 7.8602620 0.0000000
Ussa 10.8108108 73.5135135 15.6756757 0.0000000
Uvwie 0.0000000 18.1818182 63.6363636 0.0000000
Uyo 0.0000000 5.0000000 95.0000000 0.0000000
Uzo-Uwani 88.0000000 16.0000000 8.0000000 0.0000000
Vandeikya 33.9285714 55.3571429 12.5000000 0.0000000
Wamako 0.0000000 35.8024691 64.1975309 0.0000000
Wamba 0.9803922 49.0196078 48.0392157 0.0000000
Warawa 0.0000000 97.4874372 2.5125628 0.0000000
Warji 0.0000000 98.5454545 1.4545455 0.0000000
Warri North 0.0000000 3.3333333 96.6666667 0.0000000
Warri South 0.0000000 53.8461538 44.2307692 0.0000000
Warri South West 0.0000000 28.5714286 57.1428571 0.0000000
Wasagu/Danko 0.0000000 76.0000000 24.0000000 0.0000000
Wase 34.2696629 37.6404494 27.5280899 0.0000000
Wudil 0.0000000 79.1666667 12.5000000 8.3333333
Wukari 19.3415638 74.0740741 6.5843621 0.0000000
Wurno 0.0000000 15.2542373 84.7457627 0.0000000
Wushishi 0.0000000 87.7697842 12.2302158 0.0000000
Yabo 0.0000000 70.7317073 29.2682927 0.0000000
Yagba East 0.0000000 54.2372881 45.7627119 0.0000000
Yagba West 0.0000000 68.0672269 31.9327731 0.0000000
Yakurr 10.1538462 66.4615385 23.3846154 0.0000000
Yala 41.5929204 40.1179941 18.2890855 0.0000000
Yamaltu/Deba 0.0000000 81.0483871 18.9516129 0.0000000
Yankwashi 0.0000000 96.9026549 2.6548673 0.0000000
Yauri 0.0000000 88.4615385 11.5384615 0.0000000
Yenegoa 0.0000000 25.9259259 74.0740741 0.0000000
Yola North 0.0000000 34.6153846 65.3846154 0.0000000
Yola South 0.0000000 69.2307692 30.7692308 0.0000000
Yorro 16.0804020 74.8743719 9.0452261 0.0000000
Yunusari 0.0000000 0.0000000 100.0000000 0.0000000
Yusufari 0.0000000 35.7142857 64.2857143 0.0000000
Zaki 0.0000000 94.3262411 5.6737589 0.0000000
Zango 0.0000000 52.3364486 47.6635514 0.0000000
Zango-Kataf 0.0000000 94.3444730 5.6555270 0.0000000
Zaria 0.0000000 94.9843260 5.0156740 0.0000000
Zing 11.0526316 77.3684211 11.0526316 0.0000000
Zurmi 0.0000000 79.0697674 20.1550388 0.0000000
Zuru 0.0000000 90.0000000 10.0000000 0.0000000
pct_uc300 pct_uc1000 pct_ucN1000 pct_uc250 pct_urban0
Aba North 17.6470588 82.3529412 17.6470588 0.0000000 0.0000000
Aba South 12.6760563 87.3239437 12.6760563 0.0000000 5.6338028
Abadam 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
Abaji 40.3508772 59.6491228 40.3508772 0.0000000 84.2105263
Abak 8.3333333 91.6666667 8.3333333 0.0000000 83.3333333
Abakaliki 75.1072961 9.4420601 90.5579399 15.4506438 87.5536481
Abeokuta North 23.5294118 76.4705882 23.5294118 0.0000000 20.5882353
Abeokuta South 28.5714286 70.5882353 29.4117647 0.8403361 0.0000000
Abi 67.1052632 32.8947368 67.1052632 0.0000000 95.3947368
Aboh-Mbaise 34.8484848 65.1515152 34.8484848 0.0000000 72.7272727
Abua/Odual 33.3333333 66.6666667 33.3333333 0.0000000 53.8461538
Abuja Municipal 40.7407407 59.2592593 40.7407407 0.0000000 84.4444444
Adavi 46.0317460 53.9682540 46.0317460 0.0000000 28.5714286
Ado 96.0937500 3.1250000 96.8750000 0.7812500 97.6562500
Ado-Odo/Ota 34.4827586 65.5172414 34.4827586 0.0000000 54.8850575
Ado Ekiti 60.3550296 33.1360947 66.8639053 6.5088757 27.8106509
Afijio 61.3207547 38.6792453 61.3207547 0.0000000 72.6415094
Afikpo North 67.2043011 31.7204301 68.2795699 1.0752688 72.0430108
Afikpo South 78.1250000 18.7500000 81.2500000 3.1250000 82.8125000
Agaie 53.7634409 46.2365591 53.7634409 0.0000000 20.4301075
Agatu 91.3580247 8.6419753 91.3580247 0.0000000 100.0000000
Agege 8.3333333 91.6666667 8.3333333 0.0000000 0.0000000
Aguata 76.3157895 23.6842105 76.3157895 0.0000000 65.7894737
Agwara 86.7256637 13.2743363 86.7256637 0.0000000 100.0000000
Ahiazu-Mbaise 32.0000000 68.0000000 32.0000000 0.0000000 24.0000000
Ahoada East 15.7894737 84.2105263 15.7894737 0.0000000 73.6842105
Ahoada West 0.0000000 100.0000000 0.0000000 0.0000000 78.5714286
Aiyedade 72.7735369 27.2264631 72.7735369 0.0000000 83.4605598
Aiyedire 68.0000000 32.0000000 68.0000000 0.0000000 81.7142857
Aiyekire (Gbonyin) 75.0000000 20.9302326 79.0697674 4.0697674 100.0000000
Ajaokuta 44.6153846 55.3846154 44.6153846 0.0000000 100.0000000
Ajeromi-Ifelodun 87.5000000 12.5000000 87.5000000 0.0000000 0.0000000
Ajingi 94.0594059 5.9405941 94.0594059 0.0000000 100.0000000
Akamkpa 69.6000000 30.4000000 69.6000000 0.0000000 100.0000000
Akinyele 62.0111732 37.9888268 62.0111732 0.0000000 75.9776536
Akko 76.9480519 23.0519481 76.9480519 0.0000000 98.7012987
Akoko-Edo 19.3548387 80.6451613 19.3548387 0.0000000 87.0967742
Akoko North East 51.9823789 48.0176211 51.9823789 0.0000000 33.9207048
Akoko North West 67.3611111 32.6388889 67.3611111 0.0000000 81.9444444
Akoko South East 50.6410256 49.3589744 50.6410256 0.0000000 100.0000000
Akoko South West 61.7449664 38.2550336 61.7449664 0.0000000 47.9865772
Akpabuyo 54.2968750 45.7031250 54.2968750 0.0000000 100.0000000
Akuku Toru 42.8571429 57.1428571 42.8571429 0.0000000 64.2857143
Akure North 70.2127660 29.7872340 70.2127660 0.0000000 97.8723404
Akure South 43.3333333 56.6666667 43.3333333 0.0000000 10.0000000
Akwanga 63.3333333 36.6666667 63.3333333 0.0000000 79.4444444
Albasu 91.5068493 8.4931507 91.5068493 0.0000000 90.9589041
Aleiro 40.9523810 59.0476190 40.9523810 0.0000000 100.0000000
Alimosho 24.3827160 75.6172840 24.3827160 0.0000000 0.0000000
Alkaleri 90.6250000 9.3750000 90.6250000 0.0000000 86.4583333
Amuwo-Odofin 35.0000000 65.0000000 35.0000000 0.0000000 10.0000000
Anambra East 100.0000000 0.0000000 100.0000000 0.0000000 100.0000000
Anambra West 68.5185185 31.4814815 68.5185185 0.0000000 100.0000000
Anaocha 47.9452055 52.0547945 47.9452055 0.0000000 28.7671233
Andoni 11.7647059 88.2352941 11.7647059 0.0000000 64.7058824
Aninri 100.0000000 0.0000000 100.0000000 0.0000000 52.5000000
Aniocha North 0.0000000 100.0000000 0.0000000 0.0000000 100.0000000
Aniocha South 46.1538462 53.8461538 46.1538462 0.0000000 100.0000000
Anka 84.0000000 16.0000000 84.0000000 0.0000000 100.0000000
Ankpa 33.3333333 66.6666667 33.3333333 0.0000000 83.3333333
Apa 87.1794872 12.8205128 87.1794872 0.0000000 100.0000000
Apapa 100.0000000 0.0000000 100.0000000 0.0000000 0.0000000
Ardo-Kola 89.5734597 10.4265403 89.5734597 0.0000000 100.0000000
Arewa-Dandi 29.0000000 71.0000000 29.0000000 0.0000000 100.0000000
Argungu 63.0769231 36.9230769 63.0769231 0.0000000 72.3076923
Arochukwu 56.0000000 40.0000000 60.0000000 4.0000000 84.0000000
Asa 73.2394366 26.7605634 73.2394366 0.0000000 99.2957746
Asari-Toru 12.7659574 87.2340426 12.7659574 0.0000000 23.4042553
Askira/Uba 69.4444444 30.5555556 69.4444444 0.0000000 100.0000000
Atakumosa East 69.5067265 30.4932735 69.5067265 0.0000000 100.0000000
Atakumosa West 75.2032520 24.7967480 75.2032520 0.0000000 99.1869919
Atiba 53.5714286 46.4285714 53.5714286 0.0000000 40.1785714
Atigbo 69.6428571 30.3571429 69.6428571 0.0000000 82.1428571
Augie 80.5970149 19.4029851 80.5970149 0.0000000 100.0000000
Auyo 99.4604317 0.5395683 99.4604317 0.0000000 96.4028777
Awe 62.3655914 37.6344086 62.3655914 0.0000000 100.0000000
Awgu 80.7692308 19.2307692 80.7692308 0.0000000 73.0769231
Awka North 77.1428571 22.8571429 77.1428571 0.0000000 100.0000000
Awka South 70.0000000 30.0000000 70.0000000 0.0000000 47.5000000
Ayamelum 54.5454545 45.4545455 54.5454545 0.0000000 66.6666667
Babura 85.0111857 14.9888143 85.0111857 0.0000000 100.0000000
Badagry 28.5714286 71.4285714 28.5714286 0.0000000 76.1904762
Bade 67.7215190 32.2784810 67.7215190 0.0000000 72.4683544
Bagudo 80.5825243 19.4174757 80.5825243 0.0000000 78.6407767
Bagwai 93.2142857 6.7857143 93.2142857 0.0000000 93.5714286
Bakassi 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
Bakori 81.6618911 18.3381089 81.6618911 0.0000000 86.5329513
Bakura 75.6410256 24.3589744 75.6410256 0.0000000 100.0000000
Balanga 84.5283019 15.4716981 84.5283019 0.0000000 86.4150943
Bali 84.3416370 15.6583630 84.3416370 0.0000000 100.0000000
Bama 98.9898990 1.0101010 98.9898990 0.0000000 18.1818182
Barikin Ladi 86.7595819 13.2404181 86.7595819 0.0000000 95.8188153
Baruten 84.6625767 14.7239264 85.2760736 0.6134969 100.0000000
Bassa Kogi 74.6031746 25.3968254 74.6031746 0.0000000 100.0000000
Bassa Plateau 84.4221106 15.5778894 84.4221106 0.0000000 98.9949749
Batagarawa 77.0992366 22.9007634 77.0992366 0.0000000 95.4198473
Batsari 80.6250000 19.3750000 80.6250000 0.0000000 84.3750000
Bauchi 87.0967742 12.9032258 87.0967742 0.0000000 68.3870968
Baure 76.7676768 22.8956229 77.1043771 0.0000000 100.0000000
Bayo 79.1666667 20.8333333 79.1666667 0.0000000 100.0000000
Bebeji 96.3414634 3.6585366 96.3414634 0.0000000 100.0000000
Bekwara 76.9633508 23.0366492 76.9633508 0.0000000 100.0000000
Bende 56.6666667 43.3333333 56.6666667 0.0000000 81.6666667
Biase 70.2290076 29.7709924 70.2290076 0.0000000 100.0000000
Bichi 94.0828402 5.9171598 94.0828402 0.0000000 95.2662722
Bida 5.8091286 94.1908714 5.8091286 0.0000000 0.8298755
Billiri 83.5978836 16.4021164 83.5978836 0.0000000 91.0052910
Bindawa 90.4059041 9.5940959 90.4059041 0.0000000 100.0000000
Binji 57.7319588 42.2680412 57.7319588 0.0000000 100.0000000
Biriniwa 98.8402062 1.1597938 98.8402062 0.0000000 100.0000000
Birni Kudu 100.0000000 0.0000000 100.0000000 0.0000000 76.1006289
Birnin-Gwari 88.0239521 11.9760479 88.0239521 0.0000000 84.4311377
Birnin Kebbi 24.1666667 75.8333333 24.1666667 0.0000000 59.1666667
Birnin Magaji 86.5979381 13.4020619 86.5979381 0.0000000 100.0000000
Biu 72.6027397 27.3972603 72.6027397 0.0000000 17.8082192
Bodinga 26.5306122 73.4693878 26.5306122 0.0000000 74.4897959
Bogoro 91.1290323 8.8709677 91.1290323 0.0000000 100.0000000
Boki 79.8882682 20.1117318 79.8882682 0.0000000 100.0000000
Bokkos 85.9649123 14.0350877 85.9649123 0.0000000 95.1754386
Boluwaduro 67.4418605 32.5581395 67.4418605 0.0000000 88.3720930
Bomadi 75.0000000 25.0000000 75.0000000 0.0000000 100.0000000
Bonny 100.0000000 0.0000000 100.0000000 0.0000000 0.0000000
Borgu 84.2592593 15.7407407 84.2592593 0.0000000 94.4444444
Boripe 74.5762712 25.4237288 74.5762712 0.0000000 19.2090395
Bosso 93.2330827 6.7669173 93.2330827 0.0000000 95.4887218
Brass 13.6363636 86.3636364 13.6363636 0.0000000 90.9090909
Buji 98.4802432 1.5197568 98.4802432 0.0000000 100.0000000
Bukkuyum 94.4444444 5.5555556 94.4444444 0.0000000 93.3333333
Bungudu 74.2857143 25.7142857 74.2857143 0.0000000 74.2857143
Bunkure 90.5511811 9.4488189 90.5511811 0.0000000 100.0000000
Bunza 68.7116564 31.2883436 68.7116564 0.0000000 95.7055215
Bursari 38.1443299 61.8556701 38.1443299 0.0000000 100.0000000
Buruku 86.3636364 13.6363636 86.3636364 0.0000000 100.0000000
Burutu 57.1428571 42.8571429 57.1428571 0.0000000 100.0000000
Bwari 55.0847458 44.9152542 55.0847458 0.0000000 61.8644068
Calabar-Municipal 53.6585366 46.3414634 53.6585366 0.0000000 12.1951220
Calabar South 57.5342466 42.4657534 57.5342466 0.0000000 12.3287671
Chanchaga 95.8333333 4.1666667 95.8333333 0.0000000 1.3888889
Charanchi 87.8172589 12.1827411 87.8172589 0.0000000 81.7258883
Chibok 63.6363636 36.3636364 63.6363636 0.0000000 66.6666667
Chikun 100.0000000 0.0000000 100.0000000 0.0000000 100.0000000
Dala 57.2463768 42.7536232 57.2463768 0.0000000 0.0000000
Damaturu 0.0000000 100.0000000 0.0000000 0.0000000 35.8974359
Damban 85.1351351 14.8648649 85.1351351 0.0000000 70.2702703
Dambatta 82.2660099 17.7339901 82.2660099 0.0000000 84.2364532
Damboa 100.0000000 0.0000000 100.0000000 0.0000000 11.1111111
Dan Musa 76.5625000 23.4375000 76.5625000 0.0000000 100.0000000
Dandi 72.7272727 27.2727273 72.7272727 0.0000000 93.3333333
Dandume 76.5217391 23.4782609 76.5217391 0.0000000 73.0434783
Dange-Shuni 36.3636364 63.6363636 36.3636364 0.0000000 93.5064935
Danja 62.9629630 37.0370370 62.9629630 0.0000000 100.0000000
Darazo 92.2222222 7.7777778 92.2222222 0.0000000 85.5555556
Dass 95.6521739 4.3478261 95.6521739 0.0000000 89.2976589
Daura 63.1578947 36.8421053 63.1578947 0.0000000 47.3684211
Dawakin Kudu 90.5213270 9.4786730 90.5213270 0.0000000 81.5165877
Dawakin Tofa 98.6013986 1.3986014 98.6013986 0.0000000 82.5174825
Degema 25.0000000 75.0000000 25.0000000 0.0000000 75.0000000
Dekina 10.5769231 89.4230769 10.5769231 0.0000000 94.2307692
Demsa 92.3076923 7.6923077 92.3076923 0.0000000 100.0000000
Dikwa 95.0000000 5.0000000 95.0000000 0.0000000 7.5000000
Doguwa 94.5454545 5.4545455 94.5454545 0.0000000 100.0000000
Doma 25.0000000 75.0000000 25.0000000 0.0000000 44.3181818
Donga 87.5675676 12.4324324 87.5675676 0.0000000 84.8648649
Dukku 77.2058824 22.7941176 77.2058824 0.0000000 100.0000000
Dunukofia 15.5555556 84.4444444 15.5555556 0.0000000 24.4444444
Dutse 93.5064935 6.4935065 93.5064935 0.0000000 88.9610390
Dutsi 72.5000000 27.5000000 72.5000000 0.0000000 100.0000000
Dutsin-Ma 88.0258900 11.9741100 88.0258900 0.0000000 86.7313916
Eastern Obolo 24.0000000 76.0000000 24.0000000 0.0000000 100.0000000
Ebonyi 89.2307692 5.7692308 94.2307692 5.0000000 93.0769231
Edati 53.7234043 46.2765957 53.7234043 0.0000000 100.0000000
Ede North 63.8888889 36.1111111 63.8888889 0.0000000 29.6296296
Ede South 89.0410959 10.9589041 89.0410959 0.0000000 93.1506849
Edu 68.7500000 31.2500000 68.7500000 0.0000000 79.6875000
Efon 55.5555556 26.6666667 73.3333333 17.7777778 64.4444444
Egbado North 53.3333333 46.6666667 53.3333333 0.0000000 76.6666667
Egbado South 15.6250000 84.3750000 15.6250000 0.0000000 43.7500000
Egbeda 81.4432990 18.5567010 81.4432990 0.0000000 51.5463918
Egbedore 79.8742138 20.1257862 79.8742138 0.0000000 72.3270440
Egor 0.0000000 100.0000000 0.0000000 0.0000000 0.0000000
Ehime-Mbano 10.6382979 89.3617021 10.6382979 0.0000000 42.5531915
Ejigbo 92.1810700 7.8189300 92.1810700 0.0000000 61.9341564
Ekeremor 18.1818182 81.8181818 18.1818182 0.0000000 90.9090909
Eket 8.8235294 91.1764706 8.8235294 0.0000000 80.8823529
Ekiti 64.8514851 35.1485149 64.8514851 0.0000000 100.0000000
Ekiti East 60.0000000 13.3333333 86.6666667 26.6666667 52.2222222
Ekiti South West 54.9382716 38.2716049 61.7283951 6.7901235 45.0617284
Ekiti West 63.2478632 29.0598291 70.9401709 7.6923077 77.7777778
Ekwusigo 86.1111111 13.8888889 86.1111111 0.0000000 19.4444444
Eleme 50.0000000 50.0000000 50.0000000 0.0000000 50.0000000
Emohua 83.3333333 16.6666667 83.3333333 0.0000000 100.0000000
Emure 55.3846154 30.7692308 69.2307692 13.8461538 58.4615385
Enugu East 56.5217391 43.4782609 56.5217391 0.0000000 21.7391304
Enugu North 83.3333333 16.6666667 83.3333333 0.0000000 4.1666667
Enugu South 57.8947368 42.1052632 57.8947368 0.0000000 26.3157895
Epe 28.9855072 70.0483092 29.9516908 0.9661836 73.9130435
Esan Central 21.8750000 78.1250000 21.8750000 0.0000000 81.2500000
Esan North East 5.8823529 94.1176471 5.8823529 0.0000000 58.8235294
Esan South East 7.1428571 92.8571429 7.1428571 0.0000000 100.0000000
Esan West 20.5882353 79.4117647 20.5882353 0.0000000 70.5882353
Ese-Odo 18.9189189 81.0810811 18.9189189 0.0000000 99.0990991
Esit - Eket 28.9473684 71.0526316 28.9473684 0.0000000 100.0000000
Essien Udim 3.4482759 96.5517241 3.4482759 0.0000000 91.3793103
Etche 36.8421053 63.1578947 36.8421053 0.0000000 100.0000000
Ethiope East 13.6363636 86.3636364 13.6363636 0.0000000 95.4545455
Ethiope West 20.0000000 80.0000000 20.0000000 0.0000000 84.0000000
Eti-Osa 21.0526316 78.9473684 21.0526316 0.0000000 10.5263158
Etim Ekpo 9.8039216 90.1960784 9.8039216 0.0000000 100.0000000
Etinan 20.0000000 80.0000000 20.0000000 0.0000000 91.4285714
Etsako Central 25.0000000 75.0000000 25.0000000 0.0000000 100.0000000
Etsako East 9.5238095 90.4761905 9.5238095 0.0000000 88.0952381
Etsako West 0.0000000 100.0000000 0.0000000 0.0000000 40.9090909
Etung 62.4242424 37.5757576 62.4242424 0.0000000 100.0000000
Ewekoro 56.9444444 43.0555556 56.9444444 0.0000000 98.6111111
Ezeagu 64.7058824 35.2941176 64.7058824 0.0000000 100.0000000
Ezinihitte 39.2857143 60.7142857 39.2857143 0.0000000 53.5714286
Ezza North 95.3296703 2.4725275 97.5274725 2.1978022 99.1758242
Ezza South 91.4012739 4.7770701 95.2229299 3.8216561 94.2675159
Fagge 84.1269841 15.8730159 84.1269841 0.0000000 7.9365079
Fakai 85.4545455 14.5454545 85.4545455 0.0000000 80.0000000
Faskari 85.7971014 14.2028986 85.7971014 0.0000000 100.0000000
Fika 0.0000000 100.0000000 0.0000000 0.0000000 94.7368421
Fufore 46.1538462 53.8461538 46.1538462 0.0000000 100.0000000
Funakaye 73.6842105 26.3157895 73.6842105 0.0000000 67.3684211
Fune 1.4084507 98.5915493 1.4084507 0.0000000 70.4225352
Funtua 62.8571429 37.1428571 62.8571429 0.0000000 50.0000000
Gabasawa 84.3137255 15.6862745 84.3137255 0.0000000 100.0000000
Gada 66.1016949 33.8983051 66.1016949 0.0000000 100.0000000
Gagarawa 99.1011236 0.8988764 99.1011236 0.0000000 100.0000000
Gamawa 95.0338600 4.9661400 95.0338600 0.0000000 88.2618510
Ganjuwa 95.0617284 4.9382716 95.0617284 0.0000000 91.3580247
Ganye 71.4285714 28.5714286 71.4285714 0.0000000 64.2857143
Garki 97.2286374 2.7713626 97.2286374 0.0000000 100.0000000
Garko 85.3932584 14.6067416 85.3932584 0.0000000 66.2921348
Garum Mallam 92.6380368 7.3619632 92.6380368 0.0000000 100.0000000
Gashaka 90.9090909 9.0909091 90.9090909 0.0000000 100.0000000
Gassol 85.6603774 14.3396226 85.6603774 0.0000000 86.0377358
Gaya 100.0000000 0.0000000 100.0000000 0.0000000 82.4742268
Gbako 54.8571429 45.1428571 54.8571429 0.0000000 100.0000000
Gboko 73.1958763 26.8041237 73.1958763 0.0000000 80.4123711
Geidam 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
Gezawa 89.3992933 10.6007067 89.3992933 0.0000000 95.0530035
Giade 96.3636364 3.6363636 96.3636364 0.0000000 97.2727273
Girei 60.0000000 40.0000000 60.0000000 0.0000000 93.3333333
Giwa 94.5736434 5.4263566 94.5736434 0.0000000 68.2170543
Gokana 0.0000000 100.0000000 0.0000000 0.0000000 50.0000000
Gombe 48.5714286 51.4285714 48.5714286 0.0000000 0.0000000
Gombi 77.7777778 22.2222222 77.7777778 0.0000000 80.0000000
Goronyo 57.1428571 42.8571429 57.1428571 0.0000000 92.8571429
Gubio 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
Gudu 47.5000000 52.5000000 47.5000000 0.0000000 100.0000000
Gujba 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
Gulani 0.0000000 100.0000000 0.0000000 0.0000000 100.0000000
Guma 85.4700855 14.5299145 85.4700855 0.0000000 100.0000000
Gumel 62.4020888 37.5979112 62.4020888 0.0000000 64.4908616
Gummi 92.5373134 7.4626866 92.5373134 0.0000000 58.2089552
Gurara 87.8048780 12.1951220 87.8048780 0.0000000 97.5609756
Guri 98.8338192 1.1661808 98.8338192 0.0000000 100.0000000
Gusau 87.5000000 12.5000000 87.5000000 0.0000000 78.1250000
Guyuk 59.0909091 40.9090909 59.0909091 0.0000000 77.2727273
Guzamala 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
Gwadabawa 69.2982456 30.7017544 69.2982456 0.0000000 90.3508772
Gwagwalada 61.8644068 38.1355932 61.8644068 0.0000000 88.9830508
Gwale 85.4838710 14.5161290 85.4838710 0.0000000 0.0000000
Gwandu 80.6122449 19.3877551 80.6122449 0.0000000 100.0000000
Gwaram 91.3461538 8.6538462 91.3461538 0.0000000 97.1153846
Gwarzo 86.8421053 11.8421053 88.1578947 1.3157895 64.4736842
Gwer East 84.1269841 15.8730159 84.1269841 0.0000000 92.0634921
Gwer West 80.9523810 19.0476190 80.9523810 0.0000000 90.4761905
Gwiwa 98.0198020 1.9801980 98.0198020 0.0000000 100.0000000
Gwoza 65.9090909 34.0909091 65.9090909 0.0000000 52.2727273
Hadejia 93.7500000 6.2500000 93.7500000 0.0000000 5.2083333
Hawul 65.6250000 34.3750000 65.6250000 0.0000000 100.0000000
Hong 75.0000000 25.0000000 75.0000000 0.0000000 100.0000000
Ibadan North 39.3939394 60.6060606 39.3939394 0.0000000 0.0000000
Ibadan North East 37.7952756 62.2047244 37.7952756 0.0000000 0.0000000
Ibadan North West 11.4942529 88.5057471 11.4942529 0.0000000 0.0000000
Ibadan South East 41.6666667 55.5555556 44.4444444 2.7777778 0.0000000
Ibadan South West 31.7757009 68.2242991 31.7757009 0.0000000 0.9345794
Ibaji 38.2352941 61.7647059 38.2352941 0.0000000 100.0000000
Ibarapa Central 78.4688995 21.5311005 78.4688995 0.0000000 44.0191388
Ibarapa East 71.5686275 28.4313725 71.5686275 0.0000000 61.7647059
Ibarapa North 75.2525253 24.7474747 75.2525253 0.0000000 61.6161616
Ibeju/Lekki 21.0526316 78.9473684 21.0526316 0.0000000 87.2180451
Ibeno 25.0000000 75.0000000 25.0000000 0.0000000 90.0000000
Ibesikpo Asutan 1.8518519 98.1481481 1.8518519 0.0000000 92.5925926
Ibi 91.4634146 8.5365854 91.4634146 0.0000000 100.0000000
Ibiono Ibom 7.8431373 92.1568627 7.8431373 0.0000000 88.2352941
Idah 49.1525424 50.8474576 49.1525424 0.0000000 25.4237288
Idanre 87.5000000 12.5000000 87.5000000 0.0000000 90.3846154
Ideato North 64.2857143 35.7142857 64.2857143 0.0000000 71.4285714
Ideato South 43.4782609 56.5217391 43.4782609 0.0000000 56.5217391
Idemili North 87.7551020 12.2448980 87.7551020 0.0000000 4.0816327
Idemili South 83.9285714 16.0714286 83.9285714 0.0000000 0.0000000
Ido 51.4563107 48.5436893 51.4563107 0.0000000 83.4951456
Ido-Osi 50.0000000 18.7500000 81.2500000 31.2500000 70.8333333
Ifako-Ijaye 18.0327869 81.9672131 18.0327869 0.0000000 0.0000000
Ife Central 55.1948052 44.8051948 55.1948052 0.0000000 18.1818182
Ife East 54.3147208 45.6852792 54.3147208 0.0000000 21.8274112
Ife North 60.5839416 39.4160584 60.5839416 0.0000000 100.0000000
Ife South 74.7191011 25.2808989 74.7191011 0.0000000 90.4494382
Ifedayo 71.4285714 28.5714286 71.4285714 0.0000000 100.0000000
Ifedore 74.1935484 25.8064516 74.1935484 0.0000000 61.9354839
Ifelodun Kwara 69.8835275 30.1164725 69.8835275 0.0000000 89.3510815
Ifelodun Osun 64.2857143 35.7142857 64.2857143 0.0000000 30.1587302
Ifo 78.3132530 21.6867470 78.3132530 0.0000000 27.7108434
Igabi 93.1740614 6.8259386 93.1740614 0.0000000 87.3720137
Igalamela-Odolu 25.0000000 75.0000000 25.0000000 0.0000000 100.0000000
Igbo-Etiti 52.2727273 45.4545455 54.5454545 2.2727273 65.9090909
Igbo-Eze North 45.2380952 54.7619048 45.2380952 0.0000000 90.4761905
Igbo-Eze South 52.9411765 47.0588235 52.9411765 0.0000000 85.2941176
Igueben 0.0000000 100.0000000 0.0000000 0.0000000 100.0000000
Ihiala 100.0000000 0.0000000 100.0000000 0.0000000 13.7404580
Ihitte/Uboma 33.3333333 58.3333333 41.6666667 8.3333333 55.5555556
Ijebu East 80.6451613 19.3548387 80.6451613 0.0000000 100.0000000
Ijebu North 45.3125000 48.4375000 51.5625000 6.2500000 48.4375000
Ijebu North East 28.8888889 71.1111111 28.8888889 0.0000000 68.8888889
Ijebu Ode 25.8620690 74.1379310 25.8620690 0.0000000 25.8620690
Ijero 58.7378641 24.2718447 75.7281553 16.9902913 70.8737864
Ijumu 50.0000000 50.0000000 50.0000000 0.0000000 84.4262295
Ika 14.2857143 85.7142857 14.2857143 0.0000000 100.0000000
Ika North East 0.0000000 100.0000000 0.0000000 0.0000000 100.0000000
Ika South 0.0000000 100.0000000 0.0000000 0.0000000 100.0000000
Ikara 86.8613139 13.1386861 86.8613139 0.0000000 82.4817518
Ikeduru 53.3333333 46.6666667 53.3333333 0.0000000 84.4444444
Ikeja 57.1428571 42.8571429 57.1428571 0.0000000 0.0000000
Ikenne 52.6315789 47.3684211 52.6315789 0.0000000 52.6315789
Ikere 69.0721649 26.8041237 73.1958763 4.1237113 7.2164948
Ikole 60.0000000 17.2972973 82.7027027 22.7027027 64.8648649
Ikom 66.6666667 33.3333333 66.6666667 0.0000000 88.5572139
Ikono 6.3492063 93.6507937 6.3492063 0.0000000 100.0000000
Ikorodu 21.2500000 70.0000000 30.0000000 8.7500000 46.2500000
Ikot Abasi 15.5172414 84.4827586 15.5172414 0.0000000 89.6551724
Ikot Ekpene 2.1276596 97.8723404 2.1276596 0.0000000 89.3617021
Ikpoba-Okha 1.5151515 98.4848485 1.5151515 0.0000000 24.2424242
Ikwerre 45.4545455 54.5454545 45.4545455 0.0000000 78.7878788
Ikwo 89.3081761 3.1446541 96.8553459 7.5471698 98.4276730
Ikwuano 52.3364486 47.6635514 52.3364486 0.0000000 82.2429907
Ila 65.3179191 34.1040462 65.8959538 0.5780347 47.9768786
Ilaje 0.0000000 100.0000000 0.0000000 0.0000000 79.3103448
Ile-Oluji-Okeigbo 57.5539568 42.4460432 57.5539568 0.0000000 62.5899281
Ilejemeji 59.5238095 26.1904762 73.8095238 14.2857143 100.0000000
Ilesha East 47.4820144 52.5179856 47.4820144 0.0000000 7.9136691
Ilesha West 57.7319588 42.2680412 57.7319588 0.0000000 1.0309278
Illela 47.7272727 52.2727273 47.7272727 0.0000000 87.8787879
Ilorin East 64.8648649 35.1351351 64.8648649 0.0000000 45.0450450
Ilorin South 66.6666667 33.3333333 66.6666667 0.0000000 27.4509804
Ilorin West 53.5947712 46.4052288 53.5947712 0.0000000 2.6143791
Imeko-Afon 87.8787879 12.1212121 87.8787879 0.0000000 78.7878788
Ingawa 92.9292929 7.0707071 92.9292929 0.0000000 100.0000000
Ini 21.7391304 78.2608696 21.7391304 0.0000000 95.6521739
Ipokia 15.8730159 71.4285714 28.5714286 12.6984127 95.2380952
Irele 14.9425287 85.0574713 14.9425287 0.0000000 63.2183908
Irepo 61.8644068 38.1355932 61.8644068 0.0000000 24.5762712
Irepodun Kwara 62.3076923 37.6923077 62.3076923 0.0000000 88.8461538
Irepodun Osun 45.1923077 54.8076923 45.1923077 0.0000000 9.6153846
Irepodun/Ifelodun 42.4778761 25.6637168 74.3362832 31.8584071 99.1150442
Irewole 55.8558559 44.1441441 55.8558559 0.0000000 45.9459459
Isa 60.3773585 39.6226415 60.3773585 0.0000000 58.4905660
Ise/Orun 68.5714286 22.8571429 77.1428571 8.5714286 65.7142857
Iseyin 72.6415094 27.3584906 72.6415094 0.0000000 66.9811321
Ishielu 88.1147541 6.1475410 93.8524590 5.7377049 99.1803279
Isi-Uzo 87.9310345 12.0689655 87.9310345 0.0000000 87.9310345
Isiala-Ngwa North 58.4269663 41.5730337 58.4269663 0.0000000 93.2584270
Isiala-Ngwa South 26.5625000 73.4375000 26.5625000 0.0000000 89.0625000
Isiala Mbano 13.1578947 86.8421053 13.1578947 0.0000000 55.2631579
Isin 59.3548387 40.0000000 60.0000000 0.6451613 100.0000000
Isiukwuato 40.9836066 59.0163934 40.9836066 0.0000000 100.0000000
Isokan 73.9837398 26.0162602 73.9837398 0.0000000 58.5365854
Isoko North 7.6923077 92.3076923 7.6923077 0.0000000 71.7948718
Isoko South 12.5000000 87.5000000 12.5000000 0.0000000 84.3750000
Isu 62.5000000 37.5000000 62.5000000 0.0000000 54.1666667
Itas/Gadau 87.4125874 12.5874126 87.4125874 0.0000000 100.0000000
Itesiwaju 53.5211268 46.4788732 53.5211268 0.0000000 78.8732394
Itu 6.6666667 93.3333333 6.6666667 0.0000000 95.0000000
Ivo 79.5454545 15.9090909 84.0909091 4.5454545 72.7272727
Iwajowa 71.3725490 28.6274510 71.3725490 0.0000000 100.0000000
Iwo 55.0000000 45.0000000 55.0000000 0.0000000 25.6250000
Izzi 94.9843260 3.1347962 96.8652038 1.8808777 99.3730408
Jaba 96.7320261 3.2679739 96.7320261 0.0000000 84.3137255
Jada 57.1428571 42.8571429 57.1428571 0.0000000 71.4285714
Jahun 95.8333333 4.1666667 95.8333333 0.0000000 90.5303030
Jakusko 20.0000000 80.0000000 20.0000000 0.0000000 74.2857143
Jalingo 87.6811594 12.3188406 87.6811594 0.0000000 27.5362319
Jama'are 91.2698413 8.7301587 91.2698413 0.0000000 75.3968254
Jega 39.8648649 60.1351351 39.8648649 0.0000000 57.4324324
Jema'a 91.8088737 8.1911263 91.8088737 0.0000000 81.2286689
Jere 44.0789474 55.9210526 44.0789474 0.0000000 38.8157895
Jibia 83.1578947 16.8421053 83.1578947 0.0000000 72.6315789
Jos East 89.0547264 10.9452736 89.0547264 0.0000000 100.0000000
Jos North 64.6153846 35.3846154 64.6153846 0.0000000 26.1538462
Jos South 89.8395722 10.1604278 89.8395722 0.0000000 57.2192513
Kabba/Bunu 63.0136986 36.9863014 63.0136986 0.0000000 84.9315068
Kabo 100.0000000 0.0000000 100.0000000 0.0000000 100.0000000
Kachia 96.1538462 3.8461538 96.1538462 0.0000000 100.0000000
Kaduna North 77.5862069 22.4137931 77.5862069 0.0000000 0.0000000
Kaduna South 93.2432432 6.7567568 93.2432432 0.0000000 0.0000000
Kafin Hausa 100.0000000 0.0000000 100.0000000 0.0000000 100.0000000
Kafur 83.7398374 16.2601626 83.7398374 0.0000000 100.0000000
Kaga 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
Kagarko 96.2162162 3.7837838 96.2162162 0.0000000 91.3513514
Kaiama 90.2912621 9.7087379 90.2912621 0.0000000 59.2233010
Kaita 80.0664452 19.9335548 80.0664452 0.0000000 100.0000000
Kajola 71.3483146 28.6516854 71.3483146 0.0000000 29.2134831
Kajuru 95.1048951 4.8951049 95.1048951 0.0000000 100.0000000
Kala/Balge 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
Kalgo 60.0000000 40.0000000 60.0000000 0.0000000 100.0000000
Kaltungo 82.0000000 18.0000000 82.0000000 0.0000000 68.3333333
Kanam 93.1034483 6.8965517 93.1034483 0.0000000 89.3103448
Kankara 68.5039370 31.4960630 68.5039370 0.0000000 79.5275591
Kanke 93.3035714 6.6964286 93.3035714 0.0000000 100.0000000
Kankia 82.0143885 17.9856115 82.0143885 0.0000000 76.9784173
Kano Municipal 69.3965517 30.6034483 69.3965517 0.0000000 0.0000000
Karasuwa 66.1764706 33.8235294 66.1764706 0.0000000 100.0000000
Karaye 100.0000000 0.0000000 100.0000000 0.0000000 98.2300885
Karim-Lamido 80.8641975 19.1358025 80.8641975 0.0000000 100.0000000
Karu 41.5730337 58.4269663 41.5730337 0.0000000 80.8988764
Katagum 72.8813559 27.1186441 72.8813559 0.0000000 83.8983051
Katcha 89.1304348 10.8695652 89.1304348 0.0000000 100.0000000
Katsina 57.9545455 42.0454545 57.9545455 0.0000000 9.0909091
Katsina-Ala 89.2405063 10.7594937 89.2405063 0.0000000 92.4050633
Kaugama 99.4555354 0.5444646 99.4555354 0.0000000 100.0000000
Kaura 91.4893617 8.5106383 91.4893617 0.0000000 82.2695035
Kaura Namoda 89.4736842 10.5263158 89.4736842 0.0000000 86.3157895
Kauru 98.2658960 1.7341040 98.2658960 0.0000000 100.0000000
Kazaure 56.3968668 43.6031332 56.3968668 0.0000000 51.9582245
Keana 63.6363636 36.3636364 63.6363636 0.0000000 83.1168831
Kebbe 65.6250000 34.3750000 65.6250000 0.0000000 100.0000000
Keffi 22.0779221 77.9220779 22.0779221 0.0000000 11.6883117
Khana 6.6666667 93.3333333 6.6666667 0.0000000 93.3333333
Kibiya 95.7746479 3.7558685 96.2441315 0.4694836 100.0000000
Kirfi 80.4597701 18.3908046 81.6091954 1.1494253 100.0000000
Kiri Kasamma 55.7114228 44.2885772 55.7114228 0.0000000 100.0000000
Kiru 91.1894273 8.8105727 91.1894273 0.0000000 85.9030837
Kiyawa 98.3695652 1.6304348 98.3695652 0.0000000 91.8478261
Kogi 54.2857143 45.7142857 54.2857143 0.0000000 100.0000000
Koko/Besse 80.4054054 19.5945946 80.4054054 0.0000000 62.1621622
Kokona 68.5714286 31.4285714 68.5714286 0.0000000 100.0000000
Kolokuma/Opokuma 5.0000000 95.0000000 5.0000000 0.0000000 100.0000000
Konduga 66.6666667 33.3333333 66.6666667 0.0000000 100.0000000
Konshisha 95.0138504 4.9861496 95.0138504 0.0000000 97.5069252
Kontagora 95.0617284 4.9382716 95.0617284 0.0000000 9.8765432
Kosofe 43.1034483 56.8965517 43.1034483 0.0000000 0.0000000
Kubau 95.8333333 4.1666667 95.8333333 0.0000000 91.2500000
Kudan 100.0000000 0.0000000 100.0000000 0.0000000 100.0000000
Kuje 37.0629371 62.9370629 37.0629371 0.0000000 90.2097902
Kukawa 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
Kumbotso 92.1568627 7.8431373 92.1568627 0.0000000 49.0196078
Kunchi 97.6562500 2.3437500 97.6562500 0.0000000 100.0000000
Kura 89.3129771 10.6870229 89.3129771 0.0000000 49.6183206
Kurfi 86.4077670 13.5922330 86.4077670 0.0000000 100.0000000
Kurmi 92.8571429 7.1428571 92.8571429 0.0000000 100.0000000
Kusada 88.2352941 11.7647059 88.2352941 0.0000000 100.0000000
Kwali 56.1797753 43.8202247 56.1797753 0.0000000 90.4494382
Kwami 77.3333333 22.6666667 77.3333333 0.0000000 98.6666667
Kwande 86.2068966 13.7931034 86.2068966 0.0000000 98.8505747
Kware 48.0916031 51.9083969 48.0916031 0.0000000 95.4198473
Kwaya Kusar 100.0000000 0.0000000 100.0000000 0.0000000 100.0000000
Lafia 34.6863469 64.9446494 35.0553506 0.3690037 54.9815498
Lagelu 60.8000000 39.2000000 60.8000000 0.0000000 83.2000000
Lagos Island 13.5135135 86.4864865 13.5135135 0.0000000 0.0000000
Lagos Mainland 30.9523810 69.0476190 30.9523810 0.0000000 0.0000000
Lamurde 100.0000000 0.0000000 100.0000000 0.0000000 100.0000000
Langtang North 93.8666667 6.1333333 93.8666667 0.0000000 88.0000000
Langtang South 81.5217391 18.4782609 81.5217391 0.0000000 100.0000000
Lapai 62.7118644 37.2881356 62.7118644 0.0000000 66.1016949
Lau 89.2045455 10.7954545 89.2045455 0.0000000 100.0000000
Lavun 45.0000000 55.0000000 45.0000000 0.0000000 100.0000000
Lere 98.3695652 1.6304348 98.3695652 0.0000000 98.9130435
Logo 87.0748299 12.9251701 87.0748299 0.0000000 94.5578231
Lokoja 52.6315789 47.3684211 52.6315789 0.0000000 47.3684211
Machina 72.9729730 27.0270270 72.9729730 0.0000000 100.0000000
Madagali 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
Madobi 98.8571429 1.1428571 98.8571429 0.0000000 96.5714286
Mafa 0.0000000 100.0000000 0.0000000 0.0000000 100.0000000
Magama 97.0000000 3.0000000 97.0000000 0.0000000 97.0000000
Magumeri 77.7777778 22.2222222 77.7777778 0.0000000 77.7777778
Mai'adua 62.5498008 37.4501992 62.5498008 0.0000000 76.8924303
Maiduguri 17.2932331 82.7067669 17.2932331 0.0000000 3.0075188
Maigatari 93.5294118 6.4705882 93.5294118 0.0000000 79.4117647
Maiha 82.3529412 17.6470588 82.3529412 0.0000000 88.2352941
Maiyama 66.0000000 34.0000000 66.0000000 0.0000000 100.0000000
Makoda 19.8113208 80.1886792 19.8113208 0.0000000 100.0000000
Makurdi 66.3043478 33.6956522 66.3043478 0.0000000 22.8260870
Malam Madori 98.4126984 1.5873016 98.4126984 0.0000000 89.2857143
Malumfashi 87.5000000 12.5000000 87.5000000 0.0000000 75.0000000
Mangu 87.9746835 12.0253165 87.9746835 0.0000000 90.1898734
Mani 82.0689655 15.1724138 84.8275862 2.7586207 100.0000000
Maradun 79.1666667 20.8333333 79.1666667 0.0000000 100.0000000
Mariga 88.1355932 11.8644068 88.1355932 0.0000000 100.0000000
Markafi 91.2280702 8.7719298 91.2280702 0.0000000 80.7017544
Marte 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
Maru 75.4491018 24.5508982 75.4491018 0.0000000 100.0000000
Mashegu 76.7857143 23.2142857 76.7857143 0.0000000 100.0000000
Mashi 63.1147541 36.8852459 63.1147541 0.0000000 73.7704918
Matazu 84.9740933 14.5077720 85.4922280 0.5181347 100.0000000
Mayo-Belwa 91.6666667 8.3333333 91.6666667 0.0000000 100.0000000
Mbaitoli 52.7272727 47.2727273 52.7272727 0.0000000 90.9090909
Mbo 16.6666667 83.3333333 16.6666667 0.0000000 100.0000000
Michika 80.0000000 20.0000000 80.0000000 0.0000000 85.0000000
Miga 95.3125000 4.6875000 95.3125000 0.0000000 100.0000000
Mikang 91.1864407 8.8135593 91.1864407 0.0000000 100.0000000
Minjibir 90.2912621 9.7087379 90.2912621 0.0000000 100.0000000
Misau 93.8931298 6.1068702 93.8931298 0.0000000 62.5954198
Mkpat Enin 28.6956522 71.3043478 28.6956522 0.0000000 97.3913043
Moba 46.0526316 21.0526316 78.9473684 32.8947368 69.7368421
Mobbar 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
Mokwa 42.1052632 57.8947368 42.1052632 0.0000000 75.7894737
Monguno 96.1538462 3.8461538 96.1538462 0.0000000 42.3076923
Mopa-Muro 66.2790698 33.7209302 66.2790698 0.0000000 100.0000000
Moro 86.2857143 13.7142857 86.2857143 0.0000000 100.0000000
Mubi North 0.0000000 100.0000000 0.0000000 0.0000000 0.0000000
Mubi South 0.0000000 100.0000000 0.0000000 0.0000000 0.0000000
Musawa 91.6083916 8.3916084 91.6083916 0.0000000 100.0000000
Mushin 9.3750000 90.6250000 9.3750000 0.0000000 0.0000000
Muya 93.6842105 6.3157895 93.6842105 0.0000000 100.0000000
Nafada 62.8378378 37.1621622 62.8378378 0.0000000 100.0000000
Nangere 8.1081081 91.8918919 8.1081081 0.0000000 100.0000000
Nasarawa Kano 48.0000000 52.0000000 48.0000000 0.0000000 0.0000000
Nasarawa 58.6206897 41.3793103 58.6206897 0.0000000 77.2413793
Nasarawa-Eggon 52.8301887 47.1698113 52.8301887 0.0000000 88.6792453
Ndokwa East 32.5301205 53.0120482 46.9879518 14.4578313 100.0000000
Ndokwa West 15.2173913 84.7826087 15.2173913 0.0000000 95.6521739
Nembe 23.2558140 74.4186047 25.5813953 2.3255814 81.3953488
Ngala 93.0000000 7.0000000 93.0000000 0.0000000 16.0000000
Nganzai 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
Ngaski 93.9130435 6.0869565 93.9130435 0.0000000 100.0000000
Ngor-Okpala 51.4705882 48.5294118 51.4705882 0.0000000 89.7058824
Nguru 88.0630631 11.9369369 88.0630631 0.0000000 54.9549550
Ningi 96.7391304 3.2608696 96.7391304 0.0000000 77.1739130
Njaba 46.6666667 53.3333333 46.6666667 0.0000000 26.6666667
Njikoka 70.5882353 29.4117647 70.5882353 0.0000000 26.4705882
Nkanu East 96.2962963 1.8518519 98.1481481 1.8518519 100.0000000
Nkanu West 77.2151899 21.5189873 78.4810127 1.2658228 72.1518987
Nkwerre 12.1212121 87.8787879 12.1212121 0.0000000 24.2424242
Nnewi North 46.1538462 53.8461538 46.1538462 0.0000000 0.0000000
Nnewi South 93.9393939 6.0606061 93.9393939 0.0000000 51.5151515
Nsit Atai 13.2352941 86.7647059 13.2352941 0.0000000 100.0000000
Nsit Ibom 8.1632653 91.8367347 8.1632653 0.0000000 100.0000000
Nsit Ubium 9.0909091 90.9090909 9.0909091 0.0000000 100.0000000
Nsukka 60.7142857 39.2857143 60.7142857 0.0000000 64.2857143
Numan 0.0000000 100.0000000 0.0000000 0.0000000 0.0000000
Nwangele 28.5714286 71.4285714 28.5714286 0.0000000 0.0000000
Obafemi-Owode 48.4848485 51.5151515 48.4848485 0.0000000 89.3939394
Obanliku 70.3296703 29.6703297 70.3296703 0.0000000 98.3516484
Nasarawa Nasarawa 92.1686747 7.8313253 92.1686747 0.0000000 94.5783133
Obi Nasarawa 53.4090909 46.5909091 53.4090909 0.0000000 100.0000000
Obi Ngwa 43.9306358 56.0693642 43.9306358 0.0000000 98.2658960
Obia/Akpor 27.2000000 72.8000000 27.2000000 0.0000000 67.2000000
Obokun 55.4455446 44.5544554 55.4455446 0.0000000 99.0099010
Obot Akara 11.1111111 88.8888889 11.1111111 0.0000000 100.0000000
Obowo 40.9090909 59.0909091 40.9090909 0.0000000 51.5151515
Obubra 72.5321888 26.1802575 73.8197425 1.2875536 86.2660944
Obudu 66.9456067 33.0543933 66.9456067 0.0000000 89.9581590
Odeda 69.0265487 30.9734513 69.0265487 0.0000000 80.5309735
Odigbo 50.0000000 50.0000000 50.0000000 0.0000000 95.3488372
Odo-Otin 66.4473684 33.5526316 66.4473684 0.0000000 87.1710526
Odogbolu 25.9740260 72.7272727 27.2727273 1.2987013 98.7012987
Odukpani 34.7305389 65.2694611 34.7305389 0.0000000 100.0000000
Offa 60.6382979 39.3617021 60.6382979 0.0000000 18.0851064
Ofu 20.5882353 79.4117647 20.5882353 0.0000000 100.0000000
Ogba/Egbema/Ndoni 30.6451613 69.3548387 30.6451613 0.0000000 61.2903226
Ogbadibo 39.5061728 60.4938272 39.5061728 0.0000000 93.8271605
Ogbaru 54.3478261 45.6521739 54.3478261 0.0000000 28.2608696
Ogbia 87.5000000 12.5000000 87.5000000 0.0000000 100.0000000
Ogbomosho North 12.0000000 88.0000000 12.0000000 0.0000000 4.0000000
Ogbomosho South 35.3448276 64.6551724 35.3448276 0.0000000 14.6551724
Ogo Oluwa 46.2365591 53.7634409 46.2365591 0.0000000 93.0107527
Ogoja 79.8534799 20.1465201 79.8534799 0.0000000 89.3772894
Ogori/Magongo 27.0833333 70.8333333 29.1666667 2.0833333 100.0000000
Ogu/Bolo 0.0000000 100.0000000 0.0000000 0.0000000 100.0000000
Ogun waterside 13.5135135 86.4864865 13.5135135 0.0000000 100.0000000
Oguta 50.5050505 49.4949495 50.5050505 0.0000000 93.9393939
Ohafia 36.8421053 63.1578947 36.8421053 0.0000000 57.8947368
Ohaji/Egbema 81.4606742 17.9775281 82.0224719 0.5617978 100.0000000
Ohaozara 85.8181818 11.2727273 88.7272727 2.9090909 77.0909091
Ohaukwu 91.3165266 3.6414566 96.3585434 5.0420168 100.0000000
Ohimini 90.7692308 9.2307692 90.7692308 0.0000000 100.0000000
Oji-River 55.2631579 44.7368421 55.2631579 0.0000000 100.0000000
Ojo 49.4252874 50.5747126 49.4252874 0.0000000 19.5402299
Oju 94.5054945 5.4945055 94.5054945 0.0000000 95.9706960
Oke-Ero 73.9130435 25.6038647 74.3961353 0.4830918 100.0000000
Okehi 60.8108108 39.1891892 60.8108108 0.0000000 32.4324324
Okene 20.0000000 80.0000000 20.0000000 0.0000000 0.0000000
Okigwe 12.1951220 87.8048780 12.1951220 0.0000000 68.2926829
Okitipupa 16.0000000 84.0000000 16.0000000 0.0000000 78.6666667
Okobo 9.4594595 90.5405405 9.4594595 0.0000000 86.4864865
Okpe 38.4615385 61.5384615 38.4615385 0.0000000 69.2307692
Okpokwu 89.8305085 10.1694915 89.8305085 0.0000000 96.6101695
Okrika 33.3333333 50.0000000 50.0000000 16.6666667 0.0000000
Ola-oluwa 81.0810811 18.9189189 81.0810811 0.0000000 100.0000000
Olamabolo 0.0000000 100.0000000 0.0000000 0.0000000 100.0000000
Olorunda 67.4033149 32.5966851 67.4033149 0.0000000 37.0165746
Olorunsogo 62.6262626 37.3737374 62.6262626 0.0000000 48.4848485
Oluyole 86.4864865 13.5135135 86.4864865 0.0000000 55.4054054
Omala 48.6486486 51.3513514 48.6486486 0.0000000 100.0000000
Omumma 40.0000000 60.0000000 40.0000000 0.0000000 100.0000000
Ona-Ara 65.7894737 34.2105263 65.7894737 0.0000000 57.0175439
Ondo East 58.5185185 41.4814815 58.5185185 0.0000000 99.2592593
Ondo West 51.5527950 48.4472050 51.5527950 0.0000000 43.4782609
Onicha 89.9543379 4.1095890 95.8904110 5.9360731 100.0000000
Onitsha North 84.6153846 15.3846154 84.6153846 0.0000000 7.6923077
Onitsha South 50.0000000 50.0000000 50.0000000 0.0000000 0.0000000
Onna 19.0476190 80.9523810 19.0476190 0.0000000 63.4920635
Opobo/Nkoro 54.5454545 45.4545455 54.5454545 0.0000000 63.6363636
Oredo 4.3478261 95.6521739 4.3478261 0.0000000 0.0000000
Orelope 82.6530612 17.3469388 82.6530612 0.0000000 27.5510204
Orhionmwon 0.9009009 99.0990991 0.9009009 0.0000000 100.0000000
Ori Ire 84.9372385 15.0627615 84.9372385 0.0000000 100.0000000
Oriade 60.6177606 38.6100386 61.3899614 0.7722008 99.6138996
Orlu 41.1764706 58.8235294 41.1764706 0.0000000 31.3725490
Orolu 60.0000000 40.0000000 60.0000000 0.0000000 52.5000000
Oron 20.0000000 80.0000000 20.0000000 0.0000000 45.0000000
Orsu 25.0000000 75.0000000 25.0000000 0.0000000 25.0000000
Oru East 61.5384615 38.4615385 61.5384615 0.0000000 89.7435897
Oru West 65.8536585 34.1463415 65.8536585 0.0000000 58.5365854
Oruk Anam 22.0338983 77.9661017 22.0338983 0.0000000 98.3050847
Orumba North 68.1818182 31.8181818 68.1818182 0.0000000 89.3939394
Orumba South 48.4848485 51.5151515 48.4848485 0.0000000 48.4848485
Ose 48.0000000 52.0000000 48.0000000 0.0000000 100.0000000
Oshimili North 3.0303030 96.9696970 3.0303030 0.0000000 81.8181818
Oshimili South 8.6956522 91.3043478 8.6956522 0.0000000 34.7826087
Oshodi-Isolo 38.2978723 61.7021277 38.2978723 0.0000000 0.0000000
Osisioma Ngwa 29.5454545 70.4545455 29.5454545 0.0000000 68.1818182
Osogbo 45.9302326 54.0697674 45.9302326 0.0000000 12.7906977
Oturkpo 87.0967742 12.9032258 87.0967742 0.0000000 98.3870968
Ovia North East 8.4337349 91.5662651 8.4337349 0.0000000 77.1084337
Ovia South West 5.1948052 94.8051948 5.1948052 0.0000000 100.0000000
Owan East 7.9365079 92.0634921 7.9365079 0.0000000 100.0000000
Owan West 6.8181818 93.1818182 6.8181818 0.0000000 100.0000000
Owerri-Municipal 41.9354839 58.0645161 41.9354839 0.0000000 16.1290323
Owerri North 27.9411765 72.0588235 27.9411765 0.0000000 64.7058824
Owerri West 50.8196721 49.1803279 50.8196721 0.0000000 91.8032787
Owo 46.4088398 53.5911602 46.4088398 0.0000000 37.5690608
Oye 61.9718310 28.8732394 71.1267606 9.1549296 76.0563380
Oyi 38.3561644 61.6438356 38.3561644 0.0000000 41.0958904
Oyigbo 41.1764706 58.8235294 41.1764706 0.0000000 100.0000000
Oyo East 56.8181818 43.1818182 56.8181818 0.0000000 43.1818182
Oyo West 64.0625000 35.9375000 64.0625000 0.0000000 51.5625000
Oyun 71.6417910 28.3582090 71.6417910 0.0000000 89.5522388
Paikoro 89.3617021 10.6382979 89.3617021 0.0000000 69.1489362
Pankshin 91.7085427 8.2914573 91.7085427 0.0000000 94.4723618
Patani 27.2727273 72.7272727 27.2727273 0.0000000 100.0000000
Pategi 70.5454545 29.4545455 70.5454545 0.0000000 65.0909091
Port-Harcourt 69.5652174 5.2173913 94.7826087 24.3478261 2.6086957
Potiskum 25.0000000 75.0000000 25.0000000 0.0000000 51.3888889
Qua'an Pan 91.8840580 8.1159420 91.8840580 0.0000000 96.5217391
Rabah 58.7301587 41.2698413 58.7301587 0.0000000 100.0000000
Rafi 87.1212121 12.8787879 87.1212121 0.0000000 100.0000000
Rano 92.0000000 8.0000000 92.0000000 0.0000000 69.0000000
Remo North 43.1818182 56.8181818 43.1818182 0.0000000 63.6363636
Rijau 86.0927152 13.9072848 86.0927152 0.0000000 84.7682119
Rimi 71.4285714 28.5714286 71.4285714 0.0000000 100.0000000
Rimin Gado 97.5903614 1.8072289 98.1927711 0.6024096 99.3975904
Ringim 87.5000000 11.8055556 88.1944444 0.6944444 65.2777778
Riyom 89.1447368 10.8552632 89.1447368 0.0000000 100.0000000
Rogo 88.6486486 11.3513514 88.6486486 0.0000000 90.2702703
Roni 94.6666667 5.3333333 94.6666667 0.0000000 100.0000000
Sabon-Gari 76.2626263 23.7373737 76.2626263 0.0000000 26.5151515
Sabon Birni 80.5194805 19.4805195 80.5194805 0.0000000 100.0000000
Sabuwa 76.7441860 23.2558140 76.7441860 0.0000000 83.7209302
Safana 95.0310559 4.9689441 95.0310559 0.0000000 100.0000000
Sagbama 20.9677419 74.1935484 25.8064516 4.8387097 95.1612903
Sakaba 90.3448276 9.6551724 90.3448276 0.0000000 84.1379310
Saki East 58.7155963 41.2844037 58.7155963 0.0000000 100.0000000
Saki West 72.2972973 27.7027027 72.2972973 0.0000000 16.8918919
Sandamu 76.1467890 23.8532110 76.1467890 0.0000000 100.0000000
Sanga 94.1558442 5.8441558 94.1558442 0.0000000 95.4545455
Sapele 8.3333333 91.6666667 8.3333333 0.0000000 41.6666667
Sardauna 90.0000000 10.0000000 90.0000000 0.0000000 84.0000000
Shagamu 36.8852459 63.1147541 36.8852459 0.0000000 21.3114754
Shagari 17.3076923 82.6923077 17.3076923 0.0000000 100.0000000
Shanga 85.4368932 14.5631068 85.4368932 0.0000000 100.0000000
Shani 63.4146341 36.5853659 63.4146341 0.0000000 100.0000000
Shanono 77.1084337 22.8915663 77.1084337 0.0000000 100.0000000
Shelleng 88.8888889 11.1111111 88.8888889 0.0000000 88.8888889
Shendam 93.2242991 6.7757009 93.2242991 0.0000000 91.1214953
Shinkafi 88.5350318 11.4649682 88.5350318 0.0000000 78.3439490
Shira 89.7560976 10.2439024 89.7560976 0.0000000 89.2682927
Shiroro 90.8256881 9.1743119 90.8256881 0.0000000 95.4128440
Shomgom 88.8446215 11.1553785 88.8446215 0.0000000 100.0000000
Shomolu 21.4285714 78.5714286 21.4285714 0.0000000 0.0000000
Silame 46.6666667 53.3333333 46.6666667 0.0000000 100.0000000
Soba 91.7675545 8.2324455 91.7675545 0.0000000 93.7046005
Sokoto North 15.1515152 84.8484848 15.1515152 0.0000000 0.0000000
Sokoto South 32.0000000 68.0000000 32.0000000 0.0000000 0.0000000
Song 78.5714286 21.4285714 78.5714286 0.0000000 89.2857143
Southern Ijaw 64.5161290 35.4838710 64.5161290 0.0000000 96.7741935
Sule-Tankarkar 89.8969072 10.1030928 89.8969072 0.0000000 100.0000000
Suleja 80.4878049 19.5121951 80.4878049 0.0000000 19.5121951
Sumaila 96.7509025 3.2490975 96.7509025 0.0000000 94.9458484
Suru 80.0000000 20.0000000 80.0000000 0.0000000 90.0000000
Obi Benue 31.1475410 67.2131148 32.7868852 1.6393443 0.0000000
Surulere Lagos 58.2010582 41.7989418 58.2010582 0.0000000 94.7089947
Tafa 68.7500000 31.2500000 68.7500000 0.0000000 98.4375000
Tafawa-Balewa 87.5000000 12.5000000 87.5000000 0.0000000 92.9687500
Tai 0.0000000 100.0000000 0.0000000 0.0000000 100.0000000
Takai 93.0337079 6.9662921 93.0337079 0.0000000 88.3146067
Takum 85.0000000 15.0000000 85.0000000 0.0000000 71.6666667
Talata Mafara 72.1925134 27.8074866 72.1925134 0.0000000 83.4224599
Tambuwal 48.3146067 51.6853933 48.3146067 0.0000000 83.1460674
Tangaza 56.2500000 43.7500000 56.2500000 0.0000000 100.0000000
Tarauni 88.0597015 10.4477612 89.5522388 1.4925373 0.0000000
Tarka 93.7500000 6.2500000 93.7500000 0.0000000 95.9821429
Tarmua 5.5555556 94.4444444 5.5555556 0.0000000 83.3333333
Taura 99.0977444 0.9022556 99.0977444 0.0000000 100.0000000
Tofa 89.1891892 10.8108108 89.1891892 0.0000000 100.0000000
Toro 94.7368421 5.2631579 94.7368421 0.0000000 100.0000000
Toto 37.6068376 62.3931624 37.6068376 0.0000000 100.0000000
Toungo 80.0000000 20.0000000 80.0000000 0.0000000 100.0000000
Tsafe 84.9246231 15.0753769 84.9246231 0.0000000 81.9095477
Tsanyawa 96.1290323 3.8709677 96.1290323 0.0000000 81.2903226
Tudun Wada 85.7988166 14.2011834 85.7988166 0.0000000 86.9822485
Tureta 58.5585586 41.4414414 58.5585586 0.0000000 100.0000000
Udenu 72.7272727 27.2727273 72.7272727 0.0000000 77.2727273
Udi 71.4285714 28.5714286 71.4285714 0.0000000 80.0000000
Udu 72.7272727 27.2727273 72.7272727 0.0000000 81.8181818
Udung Uko 11.1111111 88.8888889 11.1111111 0.0000000 100.0000000
Ughelli North 32.0754717 60.3773585 39.6226415 7.5471698 88.6792453
Ughelli South 32.0000000 68.0000000 32.0000000 0.0000000 100.0000000
Ugwunagbo 75.0000000 25.0000000 75.0000000 0.0000000 94.3181818
Uhunmwonde 7.9365079 92.0634921 7.9365079 0.0000000 95.2380952
Ukanafun 38.0952381 61.9047619 38.0952381 0.0000000 97.6190476
Ukum 89.8734177 10.1265823 89.8734177 0.0000000 94.9367089
Ukwa East 61.1111111 38.8888889 61.1111111 0.0000000 100.0000000
Ukwa West 45.1612903 54.8387097 45.1612903 0.0000000 100.0000000
Ukwuani 20.3703704 79.6296296 20.3703704 0.0000000 90.7407407
Umu-Nneochi 65.9090909 34.0909091 65.9090909 0.0000000 84.0909091
Umuahia North 52.7027027 47.2972973 52.7027027 0.0000000 40.5405405
Umuahia South 51.4563107 48.5436893 51.4563107 0.0000000 63.1067961
Ungogo 95.3642384 4.6357616 95.3642384 0.0000000 67.5496689
Unuimo 83.3333333 16.6666667 83.3333333 0.0000000 50.0000000
Uruan 10.4166667 85.4166667 14.5833333 4.1666667 97.9166667
Urue-Offong/Oruko 17.8571429 82.1428571 17.8571429 0.0000000 96.4285714
Ushongo 92.1397380 7.8602620 92.1397380 0.0000000 100.0000000
Ussa 84.3243243 15.6756757 84.3243243 0.0000000 100.0000000
Uvwie 36.3636364 63.6363636 36.3636364 0.0000000 18.1818182
Uyo 5.0000000 95.0000000 5.0000000 0.0000000 67.5000000
Uzo-Uwani 92.0000000 8.0000000 92.0000000 0.0000000 100.0000000
Vandeikya 87.5000000 12.5000000 87.5000000 0.0000000 93.4523810
Wamako 35.8024691 64.1975309 35.8024691 0.0000000 86.4197531
Wamba 50.9803922 48.0392157 51.9607843 0.9803922 100.0000000
Warawa 97.4874372 2.5125628 97.4874372 0.0000000 98.4924623
Warji 98.5454545 1.4545455 98.5454545 0.0000000 100.0000000
Warri North 3.3333333 96.6666667 3.3333333 0.0000000 63.3333333
Warri South 55.7692308 44.2307692 55.7692308 0.0000000 28.8461538
Warri South West 42.8571429 57.1428571 42.8571429 0.0000000 100.0000000
Wasagu/Danko 76.0000000 24.0000000 76.0000000 0.0000000 77.1428571
Wase 72.4719101 27.5280899 72.4719101 0.0000000 79.7752809
Wudil 79.1666667 12.5000000 87.5000000 8.3333333 81.9444444
Wukari 93.4156379 6.5843621 93.4156379 0.0000000 67.9012346
Wurno 15.2542373 84.7457627 15.2542373 0.0000000 76.2711864
Wushishi 87.7697842 12.2302158 87.7697842 0.0000000 100.0000000
Yabo 70.7317073 29.2682927 70.7317073 0.0000000 100.0000000
Yagba East 54.2372881 45.7627119 54.2372881 0.0000000 86.4406780
Yagba West 68.0672269 31.9327731 68.0672269 0.0000000 72.2689076
Yakurr 76.6153846 23.3846154 76.6153846 0.0000000 60.3076923
Yala 81.7109145 18.2890855 81.7109145 0.0000000 94.9852507
Yamaltu/Deba 81.0483871 18.9516129 81.0483871 0.0000000 98.3870968
Yankwashi 97.3451327 2.6548673 97.3451327 0.0000000 100.0000000
Yauri 88.4615385 11.5384615 88.4615385 0.0000000 61.5384615
Yenegoa 25.9259259 74.0740741 25.9259259 0.0000000 88.8888889
Yola North 34.6153846 65.3846154 34.6153846 0.0000000 19.2307692
Yola South 69.2307692 30.7692308 69.2307692 0.0000000 30.7692308
Yorro 90.9547739 9.0452261 90.9547739 0.0000000 93.9698492
Yunusari 0.0000000 100.0000000 0.0000000 0.0000000 66.6666667
Yusufari 35.7142857 64.2857143 35.7142857 0.0000000 100.0000000
Zaki 94.3262411 5.6737589 94.3262411 0.0000000 90.7801418
Zango 52.3364486 47.6635514 52.3364486 0.0000000 83.1775701
Zango-Kataf 94.3444730 5.6555270 94.3444730 0.0000000 100.0000000
Zaria 94.9843260 5.0156740 94.9843260 0.0000000 29.1536050
Zing 88.9473684 11.0526316 88.9473684 0.0000000 85.2631579
Zurmi 79.8449612 20.1550388 79.8449612 0.0000000 100.0000000
Zuru 90.0000000 10.0000000 90.0000000 0.0000000 84.2857143
4.2.2 Trim High Correlation Variable and “shapeName”
cluster_varsTrim <- cluster_vars %>%
select(-shapeName, -pct_ucN1000, -pct_mechPump)4.2.2.1 review trimmed data table
summary(cluster_varsTrim) pct_functional pct_nonFunctional pct_unknown pct_handPump
Min. : 0.00 Min. : 0.00 Min. : 0.00 Min. : 0.00
1st Qu.: 32.61 1st Qu.: 20.77 1st Qu.: 0.00 1st Qu.: 16.70
Median : 47.41 Median : 34.89 Median : 0.00 Median : 50.99
Mean : 49.84 Mean : 35.58 Mean : 12.55 Mean : 48.73
3rd Qu.: 66.99 3rd Qu.: 50.00 3rd Qu.: 20.83 3rd Qu.: 77.78
Max. :100.00 Max. :100.00 Max. :100.00 Max. :100.00
pct_tapStand pct_uc300 pct_uc1000 pct_uc250
Min. : 0.0000 Min. : 0.00 Min. : 0.00 Min. : 0.0000
1st Qu.: 0.0000 1st Qu.: 38.67 1st Qu.: 12.20 1st Qu.: 0.0000
Median : 0.0000 Median : 65.91 Median : 31.27 Median : 0.0000
Mean : 0.5794 Mean : 60.17 Mean : 37.54 Mean : 0.6114
3rd Qu.: 0.0000 3rd Qu.: 87.02 3rd Qu.: 57.71 3rd Qu.: 0.0000
Max. :32.8947 Max. :100.00 Max. :100.00 Max. :32.8947
pct_urban0
Min. : 0.00
1st Qu.: 57.27
Median : 86.45
Mean : 72.71
3rd Qu.:100.00
Max. :100.00
5. CLUSTERING ANALYSIS
5.1 Hierarchy Clustering
There are four (4) main steps :
- compute proximity matrix.
- assign data point to a cluster.
- merge clusters based on similarity between clusters.
- update the distance matrix.
5.1.1 Standardise Data
As shown in the 4.2.3.1, there are few variables with Max. different from others. Hence, standardisation will be required prior to further analysis.
5.1.1.1 standardise with min-max method
nga_wpStd <- normalize(cluster_varsTrim)
summary(nga_wpStd) pct_functional pct_nonFunctional pct_unknown pct_handPump
Min. :0.0000 Min. :0.0000 Min. :0.0000 Min. :0.0000
1st Qu.:0.3261 1st Qu.:0.2077 1st Qu.:0.0000 1st Qu.:0.1670
Median :0.4741 Median :0.3489 Median :0.0000 Median :0.5099
Mean :0.4984 Mean :0.3558 Mean :0.1255 Mean :0.4873
3rd Qu.:0.6699 3rd Qu.:0.5000 3rd Qu.:0.2083 3rd Qu.:0.7778
Max. :1.0000 Max. :1.0000 Max. :1.0000 Max. :1.0000
pct_tapStand pct_uc300 pct_uc1000 pct_uc250
Min. :0.00000 Min. :0.0000 Min. :0.0000 Min. :0.00000
1st Qu.:0.00000 1st Qu.:0.3867 1st Qu.:0.1220 1st Qu.:0.00000
Median :0.00000 Median :0.6591 Median :0.3127 Median :0.00000
Mean :0.01761 Mean :0.6017 Mean :0.3754 Mean :0.01859
3rd Qu.:0.00000 3rd Qu.:0.8702 3rd Qu.:0.5771 3rd Qu.:0.00000
Max. :1.00000 Max. :1.0000 Max. :1.0000 Max. :1.00000
pct_urban0
Min. :0.0000
1st Qu.:0.5727
Median :0.8645
Mean :0.7271
3rd Qu.:1.0000
Max. :1.0000
5.1.1.2 standardise with Z-score method
nga_wpZ <- scale(cluster_varsTrim)
describe(nga_wpZ) vars n mean sd median trimmed mad min max range skew
pct_functional 1 774 0 1 -0.10 -0.02 1.04 -2.06 2.07 4.13 0.14
pct_nonFunctional 2 774 0 1 -0.03 -0.02 1.05 -1.71 3.10 4.81 0.23
pct_unknown 3 774 0 1 -0.62 -0.22 0.00 -0.62 4.30 4.92 2.01
pct_handPump 4 774 0 1 0.07 0.00 1.37 -1.49 1.57 3.06 -0.09
pct_tapStand 5 774 0 1 -0.19 -0.19 0.00 -0.19 10.46 10.65 7.22
pct_uc300 6 774 0 1 0.19 0.08 1.10 -2.02 1.34 3.35 -0.56
pct_uc1000 7 774 0 1 -0.21 -0.09 1.05 -1.28 2.14 3.42 0.61
pct_uc250 8 774 0 1 -0.20 -0.20 0.00 -0.20 10.37 10.57 7.10
pct_urban0 9 774 0 1 0.42 0.17 0.62 -2.23 0.84 3.06 -1.12
kurtosis se
pct_functional -0.62 0.04
pct_nonFunctional -0.42 0.04
pct_unknown 4.15 0.04
pct_handPump -1.33 0.04
pct_tapStand 58.65 0.04
pct_uc300 -0.87 0.04
pct_uc1000 -0.78 0.04
pct_uc250 57.14 0.04
pct_urban0 -0.09 0.04
5.1.1.3 visualise distribution of standardised clustering variable
-- functional water point
fwp <- ggplot(data=cluster_varsTrim,
aes(x= `pct_functional`)) +
geom_histogram(bins=20,
color="black",
fill="steelblue") +
ggtitle("Before Standardisation")
fwp_stdDf <- as.data.frame(nga_wpStd)
fwp_std <- ggplot(data=fwp_stdDf,
aes(x=`pct_functional`)) +
geom_histogram(bins=20,
color="black",
fill="steelblue") +
ggtitle("Min-Max Stdsn.")
fwp_zDf <- as.data.frame(nga_wpZ)
fwp_z <- ggplot(data=fwp_zDf,
aes(x=`pct_functional`)) +
geom_histogram(bins=20,
color="black",
fill="steelblue") +
ggtitle("Z-score Stdsn.")
ggarrange(fwp, fwp_std, fwp_z,
ncol = 3,
nrow = 1)
fwp <- ggplot(data=cluster_varsTrim,
aes(x= `pct_functional`)) +
geom_density(color="black",
fill="steelblue") +
ggtitle("Before Standardisation")
fwp_stdDf <- as.data.frame(nga_wpStd)
fwp_std <- ggplot(data=fwp_stdDf,
aes(x=`pct_functional`)) +
geom_density(color="black",
fill="steelblue") +
ggtitle("Min-Max Stdsn.")
fwp_zDf <- as.data.frame(nga_wpZ)
fwp_z <- ggplot(data=fwp_zDf,
aes(x=`pct_functional`)) +
geom_density(color="black",
fill="steelblue") +
ggtitle("Z-score Stdsn.")
ggarrange(fwp, fwp_std, fwp_z,
ncol = 3,
nrow = 1)
-- water point deployed with handpump
HP <- ggplot(data=cluster_varsTrim,
aes(x= `pct_handPump`)) +
geom_histogram(bins=20,
color="black",
fill="steelblue") +
ggtitle("Before Standardisation")
fwp_stdDf <- as.data.frame(nga_wpStd)
HP_std <- ggplot(data=fwp_stdDf,
aes(x=`pct_handPump`)) +
geom_histogram(bins=20,
color="black",
fill="steelblue") +
ggtitle("Min-Max Stdsn.")
fwp_zDf <- as.data.frame(nga_wpZ)
HP_z <- ggplot(data=fwp_zDf,
aes(x=`pct_handPump`)) +
geom_histogram(bins=20,
color="black",
fill="steelblue") +
ggtitle("Z-score Stdsn.")
ggarrange(HP, HP_std, HP_z,
ncol = 3,
nrow = 1)
HP <- ggplot(data=cluster_varsTrim,
aes(x= `pct_handPump`)) +
geom_density(color="black",
fill="steelblue") +
ggtitle("Before Standardisation")
fwp_stdDf <- as.data.frame(nga_wpStd)
HP_std <- ggplot(data=fwp_stdDf,
aes(x=`pct_handPump`)) +
geom_density(color="black",
fill="steelblue") +
ggtitle("Min-Max Stdsn.")
fwp_zDf <- as.data.frame(nga_wpZ)
HP_z <- ggplot(data=fwp_zDf,
aes(x=`pct_handPump`)) +
geom_density(color="black",
fill="steelblue") +
ggtitle("Z-score Stdsn.")
ggarrange(HP, HP_std, HP_z,
ncol = 3,
nrow = 1)
-- water point with 1000 users usage capacity
uc1000 <- ggplot(data=cluster_varsTrim,
aes(x= `pct_uc1000`)) +
geom_histogram(bins=20,
color="black",
fill="steelblue") +
ggtitle("Before Standardisation")
fwp_stdDf <- as.data.frame(nga_wpStd)
uc1000_std <- ggplot(data=fwp_stdDf,
aes(x=`pct_uc1000`)) +
geom_histogram(bins=20,
color="black",
fill="steelblue") +
ggtitle("Min-Max Stdsn.")
fwp_zDf <- as.data.frame(nga_wpZ)
uc1000_z <- ggplot(data=fwp_zDf,
aes(x=`pct_uc1000`)) +
geom_histogram(bins=20,
color="black",
fill="steelblue") +
ggtitle("Z-score Stdsn.")
ggarrange(uc1000, uc1000_std, uc1000_z,
ncol = 3,
nrow = 1)
uc1000 <- ggplot(data=cluster_varsTrim,
aes(x= `pct_uc1000`)) +
geom_density(color="black",
fill="steelblue") +
ggtitle("Before Standardisation")
fwp_stdDf <- as.data.frame(nga_wpStd)
uc1000_std <- ggplot(data=fwp_stdDf,
aes(x=`pct_uc1000`)) +
geom_density(color="black",
fill="steelblue") +
ggtitle("Min-Max Stdsn.")
fwp_zDf <- as.data.frame(nga_wpZ)
uc1000_z <- ggplot(data=fwp_zDf,
aes(x=`pct_uc1000`)) +
geom_density(color="black",
fill="steelblue") +
ggtitle("Z-score Stdsn.")
ggarrange(uc1000, uc1000_std, uc1000_z,
ncol = 3,
nrow = 1)
5.1.2 Compute Proximity Matrix
Usage of the code chunk below :
dist( ) - stats - to compute the proximity distance matrix. Among euclidean, maximum, manhattan, canberra, binary and minkowski, euclidean is used to compute proxmat_euc.
proxmat_euc <- dist(cluster_varsTrim, method = 'euclidean')5.1.3 Compute Hierarchical Clustering
Usage of the code chunk below :
hclust( ) - stats - to compute cluster with agglomeration method.
ggdendrogram( ) - ggdendro - to plot dendrogram with tools available in ggplot2.
hieClust_warD <- hclust(proxmat_euc, method = 'ward.D')
ggdendrogram(hieClust_warD,
rotate = TRUE,
size = 2,
theme_dendro = FALSE)
5.1.4 Determine Optimal Clustering Algorithm
Usage of the code chunk below :
agnes( ) - cluster - to get agglomerative coefficient of 4 clustering structure, namely “average”, “single”, “complete” and “ward”.
m <- c( "average", "single", "complete", "ward")
names(m) <- c( "average", "single", "complete", "ward")
ac <- function(x) {
agnes(cluster_varsTrim, method = x)$ac
}
map_dbl(m, ac) average single complete ward
0.9264460 0.8825086 0.9494033 0.9923235
Remarks :
Value 1 indicate strongest clustering structure.
Ward’s method provides the strongest clustering structure. Therefore, Ward’s method to be used in subsequent analysis.
5.1.5 Determine Optimal Clusters
To determine the optimal clusters to retain, following commons methods are tested :
Gap statistic
Elbow
Average Silhouette
5.1.5.1 compute Gap Statistic method
Usage of the code chunk below :
clusGap( ) - cluster - to compute the gap statistic.
set.seed(12345)
gap_stat <- clusGap(cluster_varsTrim,
FUN = hcut,
nstart = 25,
K.max = 30,
B = 50)
# Print the result
print(gap_stat, method = "firstmax")Clustering Gap statistic ["clusGap"] from call:
clusGap(x = cluster_varsTrim, FUNcluster = hcut, K.max = 30, B = 50, nstart = 25)
B=50 simulated reference sets, k = 1..30; spaceH0="scaledPCA"
--> Number of clusters (method 'firstmax'): 30
logW E.logW gap SE.sim
[1,] 9.815280 10.315513 0.5002330 0.008043258
[2,] 9.602465 10.203144 0.6006791 0.008510149
[3,] 9.510126 10.144007 0.6338806 0.010041659
[4,] 9.443727 10.094054 0.6503272 0.009408325
[5,] 9.337773 10.055036 0.7172630 0.008377048
[6,] 9.286151 10.021049 0.7348986 0.008061617
[7,] 9.226030 9.992116 0.7660864 0.007509762
[8,] 9.181055 9.967079 0.7860239 0.007406854
[9,] 9.132662 9.944999 0.8123364 0.007505962
[10,] 9.088576 9.924941 0.8363644 0.008003248
[11,] 9.057435 9.906340 0.8489044 0.008086779
[12,] 9.019733 9.888898 0.8691655 0.008378669
[13,] 8.988798 9.872948 0.8841499 0.008499708
[14,] 8.962331 9.857905 0.8955736 0.008609360
[15,] 8.932159 9.843557 0.9113980 0.008574353
[16,] 8.908477 9.830133 0.9216564 0.008442998
[17,] 8.880801 9.817233 0.9364313 0.008235439
[18,] 8.846775 9.805149 0.9583744 0.008068772
[19,] 8.828254 9.793671 0.9654167 0.007975742
[20,] 8.812263 9.782502 0.9702393 0.007904523
[21,] 8.793736 9.771938 0.9782012 0.007903082
[22,] 8.777957 9.761659 0.9837022 0.007927244
[23,] 8.762944 9.751686 0.9887413 0.007838986
[24,] 8.745719 9.741903 0.9961837 0.007850884
[25,] 8.732706 9.732508 0.9998020 0.007792150
[26,] 8.716858 9.723358 1.0064996 0.007813310
[27,] 8.703095 9.714414 1.0113191 0.007684052
[28,] 8.684688 9.705770 1.0210819 0.007586041
[29,] 8.664250 9.697408 1.0331579 0.007592467
[30,] 8.649964 9.689233 1.0392698 0.007607051
-- visualise gap_stat
Usage of the code chunk below :
fviz_nbclust( ) - factoextra - to compute and visualise the Optimal Number of clusters.
set.seed(12345)
fviz_nbclust(nga_wpZ,
kmeans,
nstart = 25,
method = "gap_stat",
nboot = 50)+
labs(subtitle = "Gap statistic method")Warning: did not converge in 10 iterations
Warning: did not converge in 10 iterations
Warning: did not converge in 10 iterations

5.1.5.2 compute and visualise Elbow method
fviz_nbclust(nga_wpZ, kmeans, method = "wss") +
geom_vline(xintercept = 4, linetype = 2)+
labs(subtitle = "Elbow method")
5.1.5.3 compute and visualise Silhouette method
fviz_nbclust(nga_wpZ, kmeans, method = "silhouette")+
labs(subtitle = "Silhouette method")
Remarks :
Given the Elbow method, Silhouette method and Gap Statistic method, the 5-cluster by Silhouette method will be used for the rest of the study.
5.1.5.4 interpret with Dendrogram
Usage of the code chunk below :
rect.hclust( ) - stats - to draw the dendrogram with a border around the selected clusters.
plot(hieClust_warD, cex = 0.6)
rect.hclust(hieClust_warD,
k = 5,
border = 2:5)
5.1.6 Visually-Driven Hierarchical Clustering Analysis
The data is loaded into a data frame, but it has to be a data matrix to plot the heatmap. Hence, the data frame will need to first transform into a matrix.
5.1.6.1 transform data frame into matrix
Usage of the code chunk below :
data.matrix( ) - base - to transform cluster_varsTrim data frame into a data matrix, and named it as nga_clustMat.
nga_clustMat <- data.matrix(cluster_varsTrim)5.1.6.2 plot interactive cluster heatmap
Usage of the code chunk below :
heatmaply( ) - heatmaply - to build an interactive cluster heatmap.
heatmaply(normalize(nga_clustMat),
Colv=NA,
dist_method = "euclidean",
hclust_method = "ward.D",
seriate = "OLO",
colors = Blues,
k_row = 5,
margins = c(NA,200,60,NA),
fontsize_row = 4,
fontsize_col = 5,
main="Geographic Segmentation of Nigeria by Water Points",
xlab = "Water Points",
ylab = "Nigeria LGA"
)Remarks :
Based on the plot above, 5 clusters to be retained for further analysis.
5.1.6.3 map the formed cluster
Usage of the code chunk below :
cutree( ) - base - to derive a 5-cluster model, and named the output as groups.
groups <- as.factor(cutree(hieClust_warD, k=5))5.1.6.4 append groups to wp_ngaTrans
nga_clust.sf <- cbind(wp_ngaTrans, as.matrix(groups)) %>%
rename(`cluster`=`as.matrix.groups.`)5.1.6.5 plot choropleth map :: nga_clust.sf
qtm(nga_clust.sf, "cluster")
Remarks :
The choropleth map above shows the fragmented clusters by the used of non-spatial clustering algorithm (hierarchical cluster analysis method).
5.2 Spatially Constrained Clustering :: SKATER Approach
SKATER function only support sp objects in SpatialPolygonDataFrame. Hence, the wp_ngaTrans has to first transform into SpatialPolygonDataFrame before proceed further.
5.2.1 Convert SF to SP Data Frame
Usage of the code chunk below :
as_Spatial( ) - sf - to convert wp_ngaTrans into nga_sp in a SP data frame.
nga.sp <- as_Spatial(wp_ngaTrans)5.2.2 Compute Neighbour List
Usage of the code chunk below :
poly2nb( ) - spdep - to compute the neighbours list from polygon list.
nga.nb <- poly2nb(nga.sp, queen = TRUE)
summary(nga.nb)Neighbour list object:
Number of regions: 774
Number of nonzero links: 4440
Percentage nonzero weights: 0.7411414
Average number of links: 5.736434
1 region with no links:
86
Link number distribution:
0 1 2 3 4 5 6 7 8 9 10 11 12 14
1 2 14 57 125 182 140 122 72 41 12 4 1 1
2 least connected regions:
138 560 with 1 link
1 most connected region:
508 with 14 links
Remarks :
There is one (1) region, i.e. #86 is without link. It has to be removed first before proceed to plot the neighbours list.
5.2.2.1 remove 0-neighbour region
wp_ngaTrans1 <- wp_ngaTrans[-86,]
cluster_varsTrim1 <- cluster_varsTrim[-86,]
nga_clust.sf1 <- nga_clust.sf[-86,]
nga_wpZ1 <- nga_wpZ[-86,]
nga.sp1 <- as_Spatial(wp_ngaTrans1)
nga.nb1 <- poly2nb(nga.sp1)
summary(nga.nb1)Neighbour list object:
Number of regions: 773
Number of nonzero links: 4440
Percentage nonzero weights: 0.7430602
Average number of links: 5.743855
Link number distribution:
1 2 3 4 5 6 7 8 9 10 11 12 14
2 14 57 125 182 140 122 72 41 12 4 1 1
2 least connected regions:
138 560 with 1 link
1 most connected region:
508 with 14 links
5.2.2.2 plot Neighbour List by Centroid Node
Usage of the code chunk below : plot the boundary first before the neighbour list object to avoid any region from being clipped away.
plot(nga.sp1,
border=grey(.5))
plot(nga.nb1,
coordinates(nga.sp1),
col="blue",
add=TRUE)
5.2.3 Compute Minimum Spanning Tree (MST)
5.2.3.1 calculate edge costs
Usage of the code chunk below :
nbcosts( ) - spdep - to compute the cost of each edge which is the distance between nodes.
edge_cost <- nbcosts(nga.nb1, cluster_varsTrim1)5.2.3.2 specify spatial weight
nb2listw( ) - spdep - to specify edge_cost as the spatial weights. Set the “style” to “B” to ensure the cost values are not row-standardised.
nga.w <- nb2listw(nga.nb1,
edge_cost,
style = "B")Warning in nb2listw(nga.nb1, edge_cost, style = "B"): zero sum general weights
summary(nga.w)Characteristics of weights list object:
Neighbour list object:
Number of regions: 773
Number of nonzero links: 4440
Percentage nonzero weights: 0.7430602
Average number of links: 5.743855
Link number distribution:
1 2 3 4 5 6 7 8 9 10 11 12 14
2 14 57 125 182 140 122 72 41 12 4 1 1
2 least connected regions:
138 560 with 1 link
1 most connected region:
508 with 14 links
Weights style: B
Weights constants summary:
n nn S0 S1 S2
B 773 597529 245120.7 38463020 406298492
5.2.3.3 compute minimum spanning tree
Usage of the code chunk below :
nbcosts( ) - spdep - to compute the minimum spanning tree.
nga_minSpanT <- mstree(nga.w)-- review class and dimension of the computed MST
class(nga_minSpanT)[1] "mst" "matrix"
dim(nga_minSpanT)[1] 772 3
head(nga_minSpanT) [,1] [,2] [,3]
[1,] 474 387 134.66287
[2,] 387 478 64.55618
[3,] 387 439 78.95255
[4,] 439 476 50.91751
[5,] 439 270 105.68114
[6,] 270 90 66.67241
5.2.3.4 plot MST Neighbour List
plot(nga.sp1, border=gray(.5))
plot.mst(nga_minSpanT,
coordinates(nga.sp1),
col="blue",
cex.lab=0.7,
cex.circles=0.005,
add=TRUE)
5.2.4 Compute Spatially Constrained Cluster
Usage of the code chunk below :
skater( ) - spdep - to compute the spatially constrained cluster.
clust5 <- spdep::skater(edges = nga_minSpanT[,1:2],
data = cluster_varsTrim1,
method = "euclidean",
ncuts = 4)
str(clust5)List of 8
$ groups : num [1:773] 3 3 1 5 4 1 2 2 1 3 ...
$ edges.groups:List of 5
..$ :List of 3
.. ..$ node: num [1:309] 773 747 492 131 382 224 413 488 439 257 ...
.. ..$ edge: num [1:308, 1:3] 131 382 224 413 257 767 439 704 476 75 ...
.. ..$ ssw : num 17013
..$ :List of 3
.. ..$ node: num [1:129] 597 315 316 557 195 571 339 744 205 213 ...
.. ..$ edge: num [1:128, 1:3] 315 316 557 195 571 15 82 579 744 351 ...
.. ..$ ssw : num 7874
..$ :List of 3
.. ..$ node: num [1:85] 364 10 729 215 337 551 102 103 66 19 ...
.. ..$ edge: num [1:84, 1:3] 23 536 578 103 19 375 727 617 188 103 ...
.. ..$ ssw : num 4545
..$ :List of 3
.. ..$ node: num [1:39] 550 202 330 287 374 732 537 586 733 201 ...
.. ..$ edge: num [1:38, 1:3] 612 586 136 245 332 429 504 537 586 616 ...
.. ..$ ssw : num 1294
..$ :List of 3
.. ..$ node: num [1:211] 67 510 401 122 24 526 475 489 663 303 ...
.. ..$ edge: num [1:210, 1:3] 67 549 510 119 639 401 556 122 693 70 ...
.. ..$ ssw : num 10380
$ not.prune : NULL
$ candidates : int [1:5] 1 2 3 4 5
$ ssto : num 52660
$ ssw : num [1:5] 52660 48724 44268 42679 41106
$ crit : num [1:2] 1 Inf
$ vec.crit : num [1:773] 1 1 1 1 1 1 1 1 1 1 ...
- attr(*, "class")= chr "skater"
5.2.4.1 tabulate cluster assignment
ccs5 <- clust5$groups
table(ccs5)ccs5
1 2 3 4 5
309 129 85 39 211
5.2.4.2 plot the pruned tree
plot(nga.sp1, border=gray(.5))
plot(clust5,
coordinates(nga.sp1),
cex.lab=.7,
groups.colors=c("red","green","blue", "brown", "pink"),
cex.circles=0.005,
add=TRUE)Warning in segments(coords[id1, 1], coords[id1, 2], coords[id2, 1],
coords[id2, : "add" is not a graphical parameter
Warning in segments(coords[id1, 1], coords[id1, 2], coords[id2, 1],
coords[id2, : "add" is not a graphical parameter
Warning in segments(coords[id1, 1], coords[id1, 2], coords[id2, 1],
coords[id2, : "add" is not a graphical parameter
Warning in segments(coords[id1, 1], coords[id1, 2], coords[id2, 1],
coords[id2, : "add" is not a graphical parameter
Warning in segments(coords[id1, 1], coords[id1, 2], coords[id2, 1],
coords[id2, : "add" is not a graphical parameter

5.2.5 Visualise SKATER Clusters in Choropleth Map
groups_mat <- as.matrix(clust5$groups)
nga_spClust.sf <- cbind(nga_clust.sf1, as.factor(groups_mat)) %>%
rename(`sp_cluster`=`as.factor.groups_mat.`)To compare the output of hierarchical clustering and spatially constrained hierarchical clustering :
hieClust_map <- qtm(nga_clust.sf1,
"cluster") +
tm_borders(alpha = 0.5)
ngaClust_map <- qtm(nga_spClust.sf,
"sp_cluster") +
tm_borders(alpha = 0.5)
tmap_arrange(hieClust_map, ngaClust_map,
asp=NA, ncol=2)Warning: One tm layer group has duplicated layer types, which are omitted. To
draw multiple layers of the same type, use multiple layer groups (i.e. specify
tm_shape prior to each of them).
Warning: One tm layer group has duplicated layer types, which are omitted. To
draw multiple layers of the same type, use multiple layer groups (i.e. specify
tm_shape prior to each of them).

5.3 Spatially Constrained Clustering :: ClustGeo Method
5.3.1 Perform Ward-like Hierarchical Clustering
Usage of the code chunk below :
hclustgeo( ) - ClustGeo - to perform a typical Ward-like hierarchical clustering.
proxmat_ngc <- dist(cluster_varsTrim1, method = 'euclidean')nonGeo_clust <- hclustgeo(proxmat_ngc)
plot(nonGeo_clust, cex = 0.5)
rect.hclust(nonGeo_clust,
k = 5,
border = 2:5)
5.3.1.1 visualise the formed clusters
groups_ngc <- as.factor(cutree(nonGeo_clust, k=5))nga_ngeo_clust.sf <- cbind(wp_ngaTrans1, as.matrix(groups_ngc)) %>%
rename(`cluster` = `as.matrix.groups_ngc.`)qtm(nga_ngeo_clust.sf, "cluster")
5.3.2 Perform Spatially Constrained Hierarchical Clustering
Usage of the code chunk below :
st_distance( ) - sf - to derive the spatial distance matrix before perform spatially constrained hierarchical clustering.
as.dist( ) - stats - to convert the data frame into matrix.
dist <- st_distance(wp_ngaTrans1, wp_ngaTrans1)
dist_mat <- as.dist(dist)5.3.2.1 determine alpha value
choicealpha( ) - psych - to determine a suitable value for the mixing parameter alpha.
cr <- choicealpha(
proxmat_ngc,
dist_mat,
range.alpha = seq(0, 1, 0.1),
K=5,
graph = TRUE)

Remarks :
With reference to the plot above, alpha = 0.4 to be used to perform spatially constrained hierarchical clustering.
5.3.2.2 compute spatially constrained hierarchical clustering
clustG <- hclustgeo(proxmat_ngc,
dist_mat,
alpha = 0.4)5.3.2.3 derive cluster object
groups_cg <- as.factor(cutree(clustG, k=5))5.3.2.4 combine group_cg with wp_ngaTrans1
wp_nga1_GClust <- cbind(wp_ngaTrans1, as.matrix(groups_cg)) %>%
rename(`cluster` = `as.matrix.groups_cg.`)5.3.2.5 plot delineated spatially constrained cluster
qtm(wp_nga1_GClust, "cluster")
5.4 Visual Interpretation of Clusters
5.4.1 Visualise Individual Clustering Variable
5.4.1.1 plot boxplot
ggplot(data = nga_ngeo_clust.sf,
aes(x = cluster, y = pct_functional)) +
geom_boxplot()
Remarks :
The boxplot reveals Cluster 5 displays the highest mean of functional water points. This is followed by Cluster 1, 3, 2, and 4.
5.4.2 Visualise Multivariate
Usage of the code chunk below :
ggparcoord( ) - GGally - to reveal clustering variables by cluster.
nga_ngeo_clust.sf1 <- nga_ngeo_clust.sf %>%
select("shapeName",
"pct_functional",
"pct_nonFunctional",
"pct_unknown",
"pct_handPump",
"pct_tapStand",
"pct_uc300",
"pct_uc1000",
"pct_uc250",
"pct_urban0",
"cluster")
head(nga_ngeo_clust.sf1,3)Simple feature collection with 3 features and 11 fields
Geometry type: MULTIPOLYGON
Dimension: XY
Bounding box: xmin: 7.307433 ymin: 5.052192 xmax: 13.83477 ymax: 13.71406
Projected CRS: Minna / Nigeria West Belt
shapeName pct_functional pct_nonFunctional pct_unknown pct_handPump
1 Aba North 41.17647 52.94118 5.882353 11.764706
2 Aba South 40.84507 46.47887 9.859155 9.859155
3 Abadam 0.00000 0.00000 0.000000 0.000000
pct_tapStand pct_uc300 pct_uc1000 pct_uc250 pct_urban0 cluster
1 0 17.64706 82.35294 0 0.000000 1
2 0 12.67606 87.32394 0 5.633803 1
3 0 0.00000 0.00000 0 0.000000 1
geometry
1 MULTIPOLYGON (((7.401109 5....
2 MULTIPOLYGON (((7.334479 5....
3 MULTIPOLYGON (((13.83477 13...
ggparcoord(data = nga_ngeo_clust.sf,
columns = c(2:19),
scale = "globalminmax",
alphaLines = 0.2,
boxplot = TRUE,
title = "Multiple Parallel Coordinates Plots of Variables by Cluster") +
facet_grid(~ cluster, scales = "fixed") +
theme(axis.text.x = element_text(angle = 30))
The parallel coordinate plot above reveals that households in Cluster 4 townships tend to own the highest number of TV and mobile-phone. On the other hand, households in Cluster 5 tends to own the lowest of all the five ICT.
Note that the scale argument of ggparcoor() provide several methods to scale the clustering variables. They are:
std: univariately, subtract mean and divide by standard deviation.
robust: univariately, subtract median and divide by median absolute deviation.
uniminmax: univariately, scale so the minimum of the variable is zero, and the maximum is one.
globalminmax: no scaling is done; the range of the graphs is defined by the global minimum and the global maximum.
center: use uniminmax to standardize vertical height, then center each variable at a value specified by the scaleSummary param.
centerObs: use uniminmax to standardize vertical height, then center each variable at the value of the observation specified by the centerObsID param
5.4.3 Compute Summary Statistics
nga_ngeo_clust.sf %>%
st_set_geometry(NULL) %>%
group_by(cluster) %>%
summarise(mean_pct_functional = mean(pct_functional),
mean_pct_nonFunctional = mean(pct_nonFunctional),
mean_pct_unknown = mean(pct_unknown),
mean_pct_handPump = mean(pct_handPump),
mean_pct_tapStand = mean(pct_tapStand),
mean_pct_uc300 = mean(pct_uc300),
mean_pct_uc1000 = mean(pct_uc1000),
mean_pct_uc250 = mean(pct_uc250),
mean_pct_urban0 = mean(pct_urban0))# A tibble: 5 × 10
cluster mean_pct_fun…¹ mean_…² mean_…³ mean_…⁴ mean_…⁵ mean_…⁶ mean_…⁷ mean_…⁸
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 1 51.7 29.0 9.25 35.0 0.525 44.1 45.5 0.539
2 2 46.2 42.2 11.3 59.9 1.00 70.6 28.3 1.03
3 3 40.5 49.4 9.15 9.87 0.304 19.2 80.4 0.345
4 4 18.4 14.6 67.0 18.1 0.337 72.1 27.5 0.410
5 5 78.9 20.7 0.295 88.7 0.0677 89.2 10.7 0.0903
# … with 1 more variable: mean_pct_urban0 <dbl>, and abbreviated variable names
# ¹mean_pct_functional, ²mean_pct_nonFunctional, ³mean_pct_unknown,
# ⁴mean_pct_handPump, ⁵mean_pct_tapStand, ⁶mean_pct_uc300, ⁷mean_pct_uc1000,
# ⁸mean_pct_uc250